Conversational Data Analysis Using Convokit (Captone Project)(Part 1)¶

Link for documentation : https://convokit.cornell.edu/documentation/tutorial.html

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import convokit
from convokit import Corpus, download
from convokit import TextParser
from convokit import Classifier
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import Perceptron
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
import spacy
from convokit import VectorClassifier
from sklearn.model_selection import train_test_split
import warnings
from sklearn import metrics
warnings.filterwarnings('ignore')

Import Politeness Strategies Transformer in Convokit

1) For more information and a quick demo refer the following:

https://github.com/CornellNLP/Cornell-Conversational-Analysis-Toolkit/blob/master/examples/politeness-strategies/politeness_demo.ipynb

2) For documentation regarding Politeness Transformer refer following resoures provided by Convokit

https://convokit.cornell.edu/documentation/politenessStrategies.html

from convokit import PolitenessStrategies

wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))

Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus

# Represent Corpus as a dataframe using get_utterances_dataframe()

df_wiki_corpus = wiki_corpus.get_utterances_dataframe()

df_wiki_corpus.head()

2. Annotate the corpus with politeness strategies¶

from convokit import TextParser

parser = TextParser(verbosity = 2000)

2a. Obtain dependency parsing for the utterances using PS strategies¶

The corpus will be annotated with Politeness strategies and Politeness markers. The Politeness strategies is essentially like a dictionary which stores feature politeness keys(like Please, Please start etc). If suppose the text contains Please, using spacy in the background convokit will tokenize the sentences and if suppose word please is in the text, the feature politeness will assign Please == 1. This will help the classfier to assign the text to the correct class. Text Parser will will first obtain dependency parses for the utterances, and then check for strategy use and then PS Strategies will be used

For instance : Politenes_strategies stores politeness strategy usage and 'politeness_markers'keeps positive occurances of each politeness strategies.It also stores the location where the word occurs

'politeness_strategies': {'featurepoliteness==Please==': 0, 'featurepoliteness==Please_start==': 0, 'featurepoliteness==HASHEDGE==': 0, 'featurepoliteness==Indirect_(btw)==': 0, 'featurepoliteness==Hedges==': 0, 'featurepoliteness==Factuality==': 0,....}

'politeness_markers': {'politenessmarkers==Please==': [], 'politenessmarkers==Please_start==': [], 'politenessmarkers==HASHEDGE==': [], 'politenessmarkers==Indirect_(btw)==': [], 'politenessmarkers==Hedges==': [], 'politenessmarkers==Factuality==': [], 'politenessmarkers==Deference==': [], 'politenessmarkers==Gratitude==': [], 'politenessmarkers==Apologizing==': [], 'politenessmarkers==1st_person_pl.==': [], 'politenessmarkers==1st_person==': [[('i', 0, 1)]], 'politenessmarkers==1st_person_start==': [], 'politenessmarkers==2nd_person==': [[('you', 0, 25)], [('you', 1, 1)]], 'politenessmarkers==2nd_person_start==': []}

The above 21 Linguistic markers are presented in [5] and below :

https://www.cs.cornell.edu/~cristian/Politeness_files/politeness.pdf

wiki_corpus = parser.transform(wiki_corpus)

2000/4353 utterances processed
4000/4353 utterances processed
4353/4353 utterances processed

# Update dataframe 
def update_dataframe(df_object,corpus):
    df_object = corpus.get_utterances_dataframe()
    return df_object

update_dataframe(df_wiki_corpus,wiki_corpus).head()

from convokit import PolitenessStrategies

ps = PolitenessStrategies()

# Set markers to true so that markers are visible
wiki_corpus = ps.transform(wiki_corpus, markers = True)

#update corpus

update_dataframe(df_wiki_corpus,wiki_corpus).head()

2b. Plot for Politeness Scores Averages¶

# Calculates strategy prevalence and plot graph if plot == True, with an optional selector that specifies 
# which utterances to include in the analysis.
data = ps.summarize(wiki_corpus)
data

plot = ps.summarize(wiki_corpus, plot = True)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-225-fbe6cbe509d4> in <module>
----> 1 plot = ps.summarize(wiki_corpus, plot = True)

~\anaconda3\lib\site-packages\convokit\politenessStrategies\politenessStrategies.py in summarize(self, corpus, selector, plot, y_lim)
    128         :return: a pandas DataFrame of scores with graph optionally outputted
    129         """
--> 130         scores = self._get_scores(corpus, selector)
    131 
    132         if plot:

~\anaconda3\lib\site-packages\convokit\politenessStrategies\politenessStrategies.py in _get_scores(self, corpus, selector)
    112 
    113         for utt in utts:
--> 114             for k, v in utt.meta[self.marker_attribute_name].items():
    115                 counts[k[21: len(k)-2]] += len(v)
    116         scores = {k: v/len(utts) for k, v in counts.items()}

~\anaconda3\lib\site-packages\convokit\model\convoKitMeta.py in __getitem__(self, item)
     16 
     17     def __getitem__(self, item):
---> 18         return dict.__getitem__(self, item)
     19 
     20     @staticmethod

KeyError: 'politeness_markers'

3. Predict Politeness Using Politeness Strategies¶

3a. Internal Convokit Classifiers¶

# Train classifier on only those records which have reponse -1 or 1
# iter_utterances() -> Iterates over the utterances in the corpus

list_filtered = []

for utterances in wiki_corpus.iter_utterances():
    if(utterances.meta["Binary"]!= 0):
        list_filtered.append(utterances)

wiki_corpus_binary =  Corpus(utterances = list_filtered)

wiki_corpus_binary.get_utterances_dataframe()[0:3]

3.aa Classifier Class (Convokit)¶

convokit_clf = Classifier(obj_type = 'utterance', pred_feats = ["politeness_strategies"],
                       labeller=lambda utt: utt.meta['Binary'] == 1)

Initialized default classification model (standard scaled logistic regression).

Cross Validation to evaluate Test Accuracy:¶

Cross Validation is performed to check how well our model will perform on unseen data

convokit_clf.evaluate_with_cv(wiki_corpus_binary)

Using corpus objects...
Running a cross-validated evaluation...
Done.

array([0.75229358, 0.76376147, 0.75      , 0.78390805, 0.77241379])

print("Mean Value for cross-validation evaluation Standardized Logistic ",np.mean(convokit_clf.evaluate_with_cv(wiki_corpus_binary)))

Using corpus objects...
Running a cross-validated evaluation...
Done.
Mean Value for cross-validation evaluation Standardized Logistic  0.7681514288727196

wiki_corpus_binary.get_utterances_dataframe()[0:3]

# size_of_corpus = wiki_corpus_binary.get_utterances_dataframe().shape[0]
# split_percent  = 0.955
# limit          = int((split_percent)*size_of_corpus)
# train_utt_id   = wiki_corpus_binary.get_utterance_ids()[0:limit]
# test_utt_id    = wiki_corpus_binary.get_utterance_ids()[limit:0]

3. ab Splitting data into training and test samples¶

def split_dataset(filtered_corpus, split_percent, size):
    
    limit          = int((split_percent)*size)
    train_utt_id   = filtered_corpus.get_utterance_ids()[0:limit]
    test_utt_id    = filtered_corpus.get_utterance_ids()[limit:]
    
    train_corpus   = Corpus(utterances = [utt for utt in filtered_corpus.iter_utterances() if utt.id in train_utt_id ])
    test_corpus    = Corpus(utterances = [utt for utt in filtered_corpus.iter_utterances() if utt.id in test_utt_id])
    
    return train_corpus,test_corpus

#Get shape of corpus from the dataframe

shape_of_df = wiki_corpus_binary.get_utterances_dataframe().shape
shape_of_df

(2178, 12)

train_corpus,test_corpus = split_dataset(wiki_corpus_binary, 0.90, shape_of_df[0])

print("Shape of training corpus ", train_corpus.get_utterances_dataframe().shape[0])
print("Shape of training corpus ", test_corpus.get_utterances_dataframe().shape[0])

Shape of training corpus  1960
Shape of training corpus  218

train_corpus.get_utterances_dataframe()[0:3]

3.ac Fit training corpus to the model¶

convokit_clf.fit(train_corpus)

<convokit.classifier.classifier.Classifier at 0x1859e5563c8>

# Predict on test corpus and get prediction score

predict_convokit = convokit_clf.transform(test_corpus)

convokit_clf.transform(wiki_corpus_binary)

<convokit.model.corpus.Corpus at 0x1859c2d1588>

wiki_corpus_binary.get_utterances_dataframe()[0:3]

3.ad Summary report which contains the predicted class and probability value¶

prediction_report = convokit_clf.summarize(predict_convokit)
prediction_report

3.ae Confusion Matrix and Classification Report for Internal Convokit Classifier¶

print("--------------------------------------------- Classification Report-------------------------------------------\n")
print(convokit_clf.classification_report(test_corpus))

--------------------------------------------- Classification Report-------------------------------------------

              precision    recall  f1-score   support

       False       0.72      0.78      0.75       106
        True       0.78      0.71      0.74       112

    accuracy                           0.75       218
   macro avg       0.75      0.75      0.75       218
weighted avg       0.75      0.75      0.75       218

print("-------------------------------------------- Confusion Matrix ------------------------------------")
print(convokit_clf.confusion_matrix(test_corpus))

-------------------------------------------- Confusion Matrix ------------------------------------
[[83 23]
 [32 80]]

# Print confusion_matrix using heat map
ax = sns.heatmap(convokit_clf.confusion_matrix(test_corpus), annot = True)
ax.set_title("Confusion Matrix for Convokit Classifier")
ax.set_xlabel("Actual")
ax.set_ylabel("Predicted ")
plt.show()

utterance_ids = wiki_corpus_binary.get_utterance_ids()
rows = []
for uid in utterance_ids:
    rows.append(wiki_corpus_binary.get_utterance(uid).meta["politeness_strategies"])
politeness_strategies = pd.DataFrame(rows, index=utterance_ids)

politeness_strategies

convokit_clf.get_coefs(politeness_strategies.columns)

3b. Sklearn ML Classifiers¶

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import Perceptron

3b.a Extract meta.politeness_strategies as feature variable¶

df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()

df_wiki_binary['meta.politeness_strategies']

id
629705    {'feature_politeness_==Please==': 0, 'feature_...
244336    {'feature_politeness_==Please==': 0, 'feature_...
214411    {'feature_politeness_==Please==': 1, 'feature_...
177439    {'feature_politeness_==Please==': 0, 'feature_...
341534    {'feature_politeness_==Please==': 0, 'feature_...
                                ...                        
60798     {'feature_politeness_==Please==': 0, 'feature_...
156734    {'feature_politeness_==Please==': 0, 'feature_...
147665    {'feature_politeness_==Please==': 0, 'feature_...
234095    {'feature_politeness_==Please==': 0, 'feature_...
563032    {'feature_politeness_==Please==': 0, 'feature_...
Name: meta.politeness_strategies, Length: 2178, dtype: object

3bb. Convert extracted feature to a matrix representation/dataframe¶

To extract these dictionary features, used DictVectorizer provided in sklearn. The DictVectorizer transformer turns mappings into Numpy and perform one hot-encoding for each of the values. For more information refer to sklearn documentation

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html

from sklearn.feature_extraction import DictVectorizer

dict_vectorizer = DictVectorizer()

dict_vector = dict_vectorizer.fit_transform(df_wiki_binary['meta.politeness_strategies'])

print(dict_vectorizer.get_feature_names())

['feature_politeness_==1st_person==', 'feature_politeness_==1st_person_pl.==', 'feature_politeness_==1st_person_start==', 'feature_politeness_==2nd_person==', 'feature_politeness_==2nd_person_start==', 'feature_politeness_==Apologizing==', 'feature_politeness_==Deference==', 'feature_politeness_==Direct_question==', 'feature_politeness_==Direct_start==', 'feature_politeness_==Factuality==', 'feature_politeness_==Gratitude==', 'feature_politeness_==HASHEDGE==', 'feature_politeness_==HASNEGATIVE==', 'feature_politeness_==HASPOSITIVE==', 'feature_politeness_==Hedges==', 'feature_politeness_==INDICATIVE==', 'feature_politeness_==Indirect_(btw)==', 'feature_politeness_==Indirect_(greeting)==', 'feature_politeness_==Please==', 'feature_politeness_==Please_start==', 'feature_politeness_==SUBJUNCTIVE==']

# Corresponding matrix to dict vectorizer

dict_vector.toarray()

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 1.],
       [1., 0., 0., ..., 1., 0., 1.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 1., 0., ..., 0., 0., 1.],
       [0., 0., 1., ..., 0., 0., 0.]])

df  = pd.DataFrame(dict_vector.toarray(), 
                           index = [x.id for x in wiki_corpus_binary.iter_utterances()],
                           columns = dict_vectorizer.get_feature_names())

df.columns

Index(['feature_politeness_==1st_person==',
       'feature_politeness_==1st_person_pl.==',
       'feature_politeness_==1st_person_start==',
       'feature_politeness_==2nd_person==',
       'feature_politeness_==2nd_person_start==',
       'feature_politeness_==Apologizing==',
       'feature_politeness_==Deference==',
       'feature_politeness_==Direct_question==',
       'feature_politeness_==Direct_start==',
       'feature_politeness_==Factuality==', 'feature_politeness_==Gratitude==',
       'feature_politeness_==HASHEDGE==', 'feature_politeness_==HASNEGATIVE==',
       'feature_politeness_==HASPOSITIVE==', 'feature_politeness_==Hedges==',
       'feature_politeness_==INDICATIVE==',
       'feature_politeness_==Indirect_(btw)==',
       'feature_politeness_==Indirect_(greeting)==',
       'feature_politeness_==Please==', 'feature_politeness_==Please_start==',
       'feature_politeness_==SUBJUNCTIVE=='],
      dtype='object')

Remap Politeness Value ( 0: Impolite , 1: Polite)¶

binary_ = df_wiki_binary['meta.Binary'].astype("category")
change_dicti = { -1 : 0, 1: 1}
binary_.replace(change_dicti , inplace = True )

df_feature = pd.concat([df , binary_], axis = 1)

df_feature

df_feature['meta.Binary'].value_counts()

0    1089
1    1089
Name: meta.Binary, dtype: int64

def convertToDictVector(dictionary_feature, convokit_corpus):
    from sklearn.feature_extraction import DictVectorizer
    
    dict_vectorizer = DictVectorizer()
    dict_vector = dict_vectorizer.fit_transform(dictionary_feature)
    df = pd.DataFrame(dict_vector.toarray(), index = [x.id for x in convokit_corpus.iter_utterances()],
                      columns = dict_vectorizer.get_feature_names())
    
    binary_feature = convokit_corpus.get_utterances_dataframe()['meta.Binary'].astype("category")
    
    df_feature = pd.concat([df, binary_feature ], axis = 1)
    return df_feature

3bc. K- Fold Cross Validation ( performed at step 3. bf)¶

K Cross Validation will be performed to check the performance of our model. For each of the models Kfold will be performed

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

k_fold_cv = KFold(n_splits = 5, random_state = 23, shuffle = True)

3bd. Spitting the corpus data into Train and Test¶

To split the data , use train_test_split provided in sklearn.model_selection. Here however, to split the data train_test_split will not be used

def split_data(df_corpus, train_split_percent, shape):
    
    limit          =  int((train_split_percent)*shape[0])
    X_train        =  df_corpus.iloc[0:limit, 0:(shape[1]-1)]
    X_test         =  df_corpus.iloc[limit:, 0:(shape[1]-1)]
    
    y_train        =  df_corpus.iloc[0:limit , (shape[1]-1): shape[1]]
    y_test         =  df_corpus.iloc[limit: , (shape[1]-1): shape[1]]
    
    return X_train, X_test, y_train, y_test

X_train, X_test, y_train, y_test = split_data(df_feature, 0.90, df_feature.shape)

display(X_train[0:3], X_test[0:3], y_train[0:3], y_test[0:3])

print("Shape of X_train" , X_train.shape)
print("Shape of X_test" , X_test.shape)
print("Shape of y_train" , y_train.shape)
print("Shape of y_test" , y_test.shape)

Shape of X_train (1960, 21)
Shape of X_test (218, 21)
Shape of y_train (1960, 1)
Shape of y_test (218, 1)

3be. Build ML Models¶

Logistic Regression¶

lr = LogisticRegression().fit(X_train,y_train)

To prevent the above warning : use ravel() function to flatten the array. The above warning can be ignored as it will not affect the model

lr_clf = LogisticRegression()

Naive Bayes Classifier¶

mb_clf = MultinomialNB()

Support Vector Machine¶

svm_clf_rbf  = SVC(kernel = 'rbf')
svm_clf_poly = SVC(kernel = 'poly')

KNeighborsClassifier¶

knn_clf = KNeighborsClassifier(n_neighbors = 3 , metric='minkowski')

DecisionTreeClassifier¶

dtree_clf = DecisionTreeClassifier(criterion ="entropy")

Multi-Layer Preceptron¶

mlp_clf_relu = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='relu', random_state = 21)
mlp_clf_tanh = MLPClassifier(hidden_layer_sizes = (30,30), max_iter = 1000,
                             activation = 'tanh', random_state = 22)

Perceptron¶

precp_clf = Perceptron()

Store Classifiers in a list so Looping operations can be performed to reduce reduntant code¶

list_class_polite = [lr_clf, mb_clf, svm_clf_rbf, svm_clf_poly, knn_clf, dtree_clf, mlp_clf_relu,
                       mlp_clf_tanh, precp_clf ]

3bf. Validation of ML MODELS Using K Fold Validation¶

X_features  = df_feature.drop(columns = ['meta.Binary'])
y_response = df_feature['meta.Binary']

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
              ]

def crossValidationKFold(model, X_features, y_response, model_name):
    
    from sklearn.model_selection import KFold
    from sklearn.model_selection import cross_val_score
    
    k_fold_cv = KFold(n_splits = 5, shuffle = True, random_state = 15)
    
    acc_valscore_ =  cross_val_score(estimator = model, X = X_features, y = y_response,
                                 cv = k_fold_cv, n_jobs = -1, scoring = "accuracy")
    precision_valscore_ =  cross_val_score(estimator = model, X = X_features, y = y_response,
                                 cv = k_fold_cv, n_jobs = -1, scoring = "precision")
    #get name of the model
   
    print(f"\n******************************** {model_name} Accuracy/Precision Scores *****************************************\n")
    print("List of Accuracy Value scores " ,acc_valscore_)
    print("\nMean of Accuracy Score ", acc_valscore_.mean())
    print("\nMean of Precision Score ", precision_valscore_.mean())
    
    

    
    return model_name, acc_valscore_.mean(), acc_valscore_, precision_valscore_.mean()

def KFoldAccuracyScore(list_class_polite, X_features, y_response):
    
    import pandas as pd
    mylist = []
    
    count = 0
    for model in list_class_polite:
        modelname_, acc_valscore_mean, valscore_, precision_valscore_mean = crossValidationKFold(model, X_features, y_response, model_names[count])
        mylist.append([modelname_, acc_valscore_mean, precision_valscore_mean])
        count+= 1
    
    dataframe = pd.DataFrame(mylist, columns = ['Classifier Names', 'Accuracy Score', 'Precision Score'])
    
    return(dataframe)

accuracy_result = KFoldAccuracyScore(list_class_polite, X_features, y_response)
print("\n\n********************************** K Fold Accuracy and Precision Scores ************************************")
display(accuracy_result)

******************************** Logistic Regression  Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.75       0.76834862 0.76834862 0.76781609 0.76551724]

Mean of Accuracy Score  0.764006116207951

Mean of Precision Score  0.7829084110131185

******************************** Naive Bayes Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.77293578 0.70412844 0.73853211 0.76551724 0.74712644]

Mean of Accuracy Score  0.7456480016872298

Mean of Precision Score  0.74006242922701

******************************** Support Vector Machine (Kernel= (RBF)) Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.76376147 0.77752294 0.73394495 0.74712644 0.77701149]

Mean of Accuracy Score  0.7598734577665296

Mean of Precision Score  0.7917968342887894

******************************** Support Vector Machine (Kernel = Polynomial) Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.74311927 0.76834862 0.74311927 0.74942529 0.77701149]

Mean of Accuracy Score  0.7562047875144996

Mean of Precision Score  0.8079460708295525

******************************** K-Nearest Neighbour Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.71100917 0.73165138 0.66513761 0.69425287 0.68505747]

Mean of Accuracy Score  0.6974217019930402

Mean of Precision Score  0.7289115387951303

******************************** Decision Tree Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.72247706 0.74541284 0.68348624 0.71034483 0.71264368]

Mean of Accuracy Score  0.7148729305072233

Mean of Precision Score  0.7436079160411841

******************************** MLP(Multi-Layer Preceptron(Relu)) Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.72477064 0.75458716 0.72247706 0.73563218 0.74252874]

Mean of Accuracy Score  0.7359991563851102

Mean of Precision Score  0.7516631590273097

******************************** MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.72706422 0.74770642 0.70183486 0.71494253 0.73563218]

Mean of Accuracy Score  0.7254360434461669

Mean of Precision Score  0.7357896018599673

******************************** Perceptron Accuracy/Precision Scores *****************************************

List of Accuracy Value scores  [0.73165138 0.69495413 0.71559633 0.72183908 0.68045977]

Mean of Accuracy Score  0.7089001370874196

Mean of Precision Score  0.6694902560898716


********************************** K Fold Accuracy and Precision Scores ************************************

3bg. Fit Machine Learning Models¶

lr= lr.fit(X_train,y_train)

lr_clf        =  lr_clf.fit(X_train,y_train.values.ravel())
mb_clf        =  mb_clf.fit(X_train,y_train.values.ravel())
svm_clf_rbf   =  svm_clf_rbf.fit(X_train,y_train.values.ravel())
svm_clf_poly  =  svm_clf_poly.fit(X_train,y_train.values.ravel())
knn_clf       =  knn_clf.fit(X_train,y_train.values.ravel())
dtree_clf     =  dtree_clf.fit(X_train,y_train.values.ravel())
mlp_clf_relu  =  mlp_clf_relu.fit(X_train,y_train.values.ravel())
mlp_clf_tanh  =  mlp_clf_tanh.fit(X_train,y_train.values.ravel())
precp_clf     =  precp_clf.fit(X_train,y_train.values.ravel())

def generateModels(X_train,y_train):
    
    from sklearn.linear_model import LogisticRegression
    from sklearn.naive_bayes import MultinomialNB
    from sklearn.svm import SVC
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.neural_network import MLPClassifier
    from sklearn.linear_model import Perceptron
    
    
    
    lr_clf        =  LogisticRegression().fit(X_train,y_train.values.ravel())
    mb_clf        =  MultinomialNB().fit(X_train,y_train.values.ravel())
    svm_clf_rbf   =  SVC(kernel = 'rbf').fit(X_train,y_train.values.ravel())
    svm_clf_poly  =  SVC(kernel = 'poly').fit(X_train,y_train.values.ravel())
    knn_clf       =  KNeighborsClassifier(n_neighbors = 3, weights='uniform', 
                                          metric='minkowski').fit(X_train,y_train.values.ravel())
    dtree_clf     =  DecisionTreeClassifier(criterion ="entropy").fit(X_train,y_train.values.ravel())
    mlp_clf_relu  =  MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='relu', random_state = 21).fit(X_train,y_train.values.ravel())
    mlp_clf_tanh  =  MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='tanh', random_state = 22).fit(X_train,y_train.values.ravel())
    precp_clf     =  Perceptron().fit(X_train,y_train.values.ravel())
    
    
    return list([lr_clf, mb_clf, svm_clf_rbf, svm_clf_poly, knn_clf,dtree_clf, 
                mlp_clf_relu, mlp_clf_tanh, precp_clf])

3bf. Prediction using above ML Models¶

def performPrediction(classifier, X_test, y_test, color, type_classifier):
    
    from sklearn.metrics import confusion_matrix,classification_report, accuracy_score
    import matplotlib.pyplot as plt
    import seaborn as sns
    from sklearn import metrics

    # Display Confusion Matris
    predicted_values = classifier.predict(X_test)
    print("\n\n----------------Confusion Matrix for {} Classifier--------------------".format(type_classifier))
    clf_matrix = confusion_matrix(y_test , predicted_values)
    fig = sns.heatmap(clf_matrix, annot = True, cmap = color, fmt = '.4g')
    fig.set_title(f"Seaborn Confusion Matrix for  {type_classifier} Classifier\n")
    fig.set_xlabel("Predicted Values")
    fig.set_ylabel("Actual Values")
  
    
    fig.xaxis.set_ticklabels(["False","True"])
    fig.yaxis.set_ticklabels(["False","True"])
    plt.show()
    
    # Display Classification Report 
    print(f"\n\n\tClassification Report for {type_classifier} Classifier\n")
    print(classification_report(y_test, predicted_values, digits = 3))
    
    
    #Display ROC-AUC Curve
    accuracy = accuracy_score(y_test,predicted_values ) 
    fpr, tpr, _ = metrics.roc_curve(y_test,  predicted_values)
    auc = metrics.roc_auc_score(y_test, predicted_values)
    
    print()
    #create ROC curve
    plt.plot(fpr,tpr,label=f"AUC for {type_classifier} ="+str(auc))
    plt.ylabel('True Positive Rate')
    plt.xlabel('False Positive Rate')
    plt.legend(loc=4)
    plt.title(f'AUC-ROC plot for {type_classifier} ')
    plt.show()
    
    return predicted_values

Display Metrics Models¶

predicted_values_lr = performPrediction(lr_clf,X_test, y_test,"Blues","Logistic Regression ")
print('\n\n')


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.711     0.764     0.736       106
           1      0.760     0.705     0.731       112

    accuracy                          0.734       218
   macro avg      0.735     0.735     0.734       218
weighted avg      0.736     0.734     0.734       218

predicted_values_mb = performPrediction(mb_clf,X_test, y_test,"Greens", "Naive Bayes")


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.738     0.717     0.727       106
           1      0.739     0.759     0.749       112

    accuracy                          0.739       218
   macro avg      0.738     0.738     0.738       218
weighted avg      0.739     0.739     0.738       218

predicted_svm_clf_rbf = performPrediction(svm_clf_rbf,X_test, y_test, "Greys", "Support Vector Machine (Kernel= (RBF))")


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.711     0.811     0.758       106
           1      0.794     0.688     0.737       112

    accuracy                          0.748       218
   macro avg      0.752     0.749     0.747       218
weighted avg      0.753     0.748     0.747       218

predicted_svm_clf_poly = performPrediction(svm_clf_poly,X_test, y_test, "Oranges", "Support Vector Machine (Kernel = Polynomial)")


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.704     0.830     0.762       106
           1      0.806     0.670     0.732       112

    accuracy                          0.748       218
   macro avg      0.755     0.750     0.747       218
weighted avg      0.757     0.748     0.746       218

predicted_knn_clf =  performPrediction(knn_clf,X_test, y_test, "Blues" ,"K-Nearest Neighbour")


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.635     0.755     0.690       106
           1      0.717     0.589     0.647       112

    accuracy                          0.670       218
   macro avg      0.676     0.672     0.668       218
weighted avg      0.677     0.670     0.668       218

predicted_precp_clf = performPrediction(precp_clf, X_test, y_test,"BuPu","Perceptron")


----------------Confusion Matrix for Perceptron Classifier--------------------


	Classification Report for Perceptron Classifier

              precision    recall  f1-score   support

           0      0.906     0.274     0.420       106
           1      0.586     0.973     0.732       112

    accuracy                          0.633       218
   macro avg      0.746     0.623     0.576       218
weighted avg      0.742     0.633     0.580       218

predicted_dtree_clf = performPrediction(dtree_clf,X_test, y_test, "Paired", "Decision Tree")


----------------Confusion Matrix for Decision Tree Classifier--------------------


	Classification Report for Decision Tree Classifier

              precision    recall  f1-score   support

           0      0.693     0.745     0.718       106
           1      0.740     0.688     0.713       112

    accuracy                          0.716       218
   macro avg      0.717     0.716     0.716       218
weighted avg      0.717     0.716     0.716       218

predicted_mlp_clf_relu = performPrediction(mlp_clf_relu, X_test, y_test,"Greens", "MLP(Multi-Layer Preceptron(Relu))")


----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------


	Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier

              precision    recall  f1-score   support

           0      0.758     0.708     0.732       106
           1      0.739     0.786     0.762       112

    accuracy                          0.748       218
   macro avg      0.749     0.747     0.747       218
weighted avg      0.748     0.748     0.747       218

predicted_mlp_clf_tanh = performPrediction(mlp_clf_tanh, X_test, y_test,"Purples", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))")


----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------


	Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier

              precision    recall  f1-score   support

           0      0.755     0.726     0.740       106
           1      0.750     0.777     0.763       112

    accuracy                          0.752       218
   macro avg      0.752     0.752     0.752       218
weighted avg      0.752     0.752     0.752       218

4. Using Model to predict Utterances Stanford CORPUS Dataset¶

4b. Sample Text Prediction¶

sample_text = "Hi, please let me know if you would proofread my article. It will be great if you could help"

spacy_nlp = spacy.load('en_core_web_sm')
utterance = ps.transform_utterance(sample_text)
data = utterance.meta['politeness_strategies']
dict_vect = DictVectorizer()
dict_vect = dict_vect.fit_transform(data)
dict_vect

<1x21 sparse matrix of type '<class 'numpy.float64'>'
	with 21 stored elements in Compressed Sparse Row format>

#predicts text as polite

print(lr_clf.predict(dict_vect.toarray()))

[1]

4b. Prediction on Subset of features in Stack Corpus¶

Load stack exchange Corpus¶

stack_corpus = Corpus(filename=download("stack-exchange-politeness-corpus"))

Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus

# Train classifier on only those records which have reponse -1 or 1
# iter_utterances() -> Iterates over the utterances in the corpus

list_filtered = []

for utterances in stack_corpus.iter_utterances():
    if(utterances.meta["Binary"]!= 0):
        list_filtered.append(utterances)

stack_corpus = Corpus(utterances = list_filtered)

stack_corpus.get_utterances_dataframe()[10:20]

Parse the text using Text Parser Transformer and then use Politeness Strategies¶

stack_text_parser = TextParser(verbosity = 1000)

# Transformer will annotate  data with 21 politeness markers
stack_polite_parser = PolitenessStrategies()

stack_corpus = stack_text_parser.transform(stack_corpus)

1000/3302 utterances processed
2000/3302 utterances processed
3000/3302 utterances processed
3302/3302 utterances processed

stack_corpus = stack_polite_parser.transform(stack_corpus, markers = True)

# display utterance in a dataframe

df_stack_corpus = stack_corpus.get_utterances_dataframe()
df_stack_corpus[0:4]

# Using convertToDictVector() defined above, again to extract dictionary features 

df_standford_features = convertToDictVector(df_stack_corpus['meta.politeness_strategies'], stack_corpus)
df_standford_features['meta.Binary'].replace(change_dicti, inplace = True)
df_standford_features

stack_corpus.get_utterances_dataframe().shape

(3302, 12)

4bb. Build Models on Stanford Politeness Corpus¶

X_train, X_test, y_train, y_test = split_data(df_standford_features, 0.90, df_standford_features.shape)

# generate models and store the objects in a list

model_list_standford = generateModels(X_train,y_train)

model_list_standford

[LogisticRegression(),
 MultinomialNB(),
 SVC(),
 SVC(kernel='poly'),
 KNeighborsClassifier(n_neighbors=3),
 DecisionTreeClassifier(criterion='entropy'),
 MLPClassifier(hidden_layer_sizes=(30, 30), max_iter=1000, random_state=21),
 MLPClassifier(activation='tanh', hidden_layer_sizes=(30, 30), max_iter=1000,
               random_state=22),
 Perceptron()]

# Following code will iterate over the list and generate classification report, confusion matrix, ROC curve
# for all models.

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
               "Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
               "Perceptron"
              ]

count = 0
for model in model_list_standford:
    performPrediction(model, X_test, y_test, "ocean", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.571     0.560     0.565       159
           1      0.600     0.610     0.605       172

    accuracy                          0.586       331
   macro avg      0.585     0.585     0.585       331
weighted avg      0.586     0.586     0.586       331


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.558     0.572     0.565       159
           1      0.595     0.581     0.588       172

    accuracy                          0.577       331
   macro avg      0.577     0.577     0.577       331
weighted avg      0.577     0.577     0.577       331


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.582     0.516     0.547       159
           1      0.595     0.657     0.624       172

    accuracy                          0.589       331
   macro avg      0.588     0.586     0.585       331
weighted avg      0.588     0.589     0.587       331


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.547     0.547     0.547       159
           1      0.581     0.581     0.581       172

    accuracy                          0.565       331
   macro avg      0.564     0.564     0.564       331
weighted avg      0.565     0.565     0.565       331


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.571     0.610     0.590       159
           1      0.615     0.576     0.595       172

    accuracy                          0.592       331
   macro avg      0.593     0.593     0.592       331
weighted avg      0.594     0.592     0.592       331

stack_corpus.get_utterances_dataframe().shape

(3302, 12)

X_train.shape

(2971, 21)

X_test.shape

(331, 21)

4bc. Instead of using training set of 90% use training set of 95% and compare model performance¶

X_train_2, X_test_2, y_train_2, y_test_2 = split_data(df_standford_features, 0.95, df_standford_features.shape)
model_list_standford_2 = generateModels(X_train_2 , y_train_2)

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
               "Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
               "Perceptron"
              ]


count = 0
for model in model_list_standford_2:
    performPrediction(model, X_test_2, y_test_2, "ocean", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.787     0.565     0.658        85
           1      0.648     0.840     0.731        81

    accuracy                          0.699       166
   macro avg      0.717     0.702     0.694       166
weighted avg      0.719     0.699     0.693       166


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.776     0.529     0.629        85
           1      0.630     0.840     0.720        81

    accuracy                          0.681       166
   macro avg      0.703     0.684     0.674       166
weighted avg      0.705     0.681     0.673       166


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.797     0.553     0.653        85
           1      0.645     0.852     0.734        81

    accuracy                          0.699       166
   macro avg      0.721     0.702     0.693       166
weighted avg      0.723     0.699     0.692       166


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.742     0.576     0.649        85
           1      0.640     0.790     0.707        81

    accuracy                          0.681       166
   macro avg      0.691     0.683     0.678       166
weighted avg      0.692     0.681     0.677       166


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.725     0.588     0.649        85
           1      0.639     0.765     0.697        81

    accuracy                          0.675       166
   macro avg      0.682     0.677     0.673       166
weighted avg      0.683     0.675     0.672       166

4bc. Using Cross Domain models i.e Linguistic Marker Model built on Wikipedia Politness Corpus to Predict the outcome variable for Standford Corpus¶

# build model on all the dataset now instead of splitting it 
# df_feature variable is dataset for wikipedia politeness corpus
df_feature.shape

(2178, 22)

X_features  = df_feature.drop(columns = ['meta.Binary'])
y_response = df_feature['meta.Binary']

list_class_polite_2 = generateModels(X_features , y_response)

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
               "Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
               "Perceptron"
              ]

X_ = df_standford_features.drop(['meta.Binary'], axis = 1)
y_ = df_standford_features['meta.Binary']


count = 0
for model in list_class_polite_2:
    performPrediction(model, X_, y_, "Spectral", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.546     0.822     0.656      1651
           1      0.639     0.316     0.423      1651

    accuracy                          0.569      3302
   macro avg      0.592     0.569     0.539      3302
weighted avg      0.592     0.569     0.539      3302


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.576     0.741     0.648      1651
           1      0.636     0.454     0.530      1651

    accuracy                          0.597      3302
   macro avg      0.606     0.597     0.589      3302
weighted avg      0.606     0.597     0.589      3302


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.551     0.810     0.656      1651
           1      0.641     0.339     0.444      1651

    accuracy                          0.575      3302
   macro avg      0.596     0.575     0.550      3302
weighted avg      0.596     0.575     0.550      3302


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.550     0.828     0.661      1651
           1      0.652     0.322     0.431      1651

    accuracy                          0.575      3302
   macro avg      0.601     0.575     0.546      3302
weighted avg      0.601     0.575     0.546      3302


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.542     0.772     0.637      1651
           1      0.604     0.348     0.441      1651

    accuracy                          0.560      3302
   macro avg      0.573     0.560     0.539      3302
weighted avg      0.573     0.560     0.539      3302

!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Caution : Recall and Precision is extremely poor for above models (compared to baseline Linguistic Marker model)!!!!!!!!!!!!!!!!!!!!!!!!!!!

5. Building ML Models using BoW¶

5a. Load Wikipedia Politeness Corpus and perform Text Parsing on the dataset¶

Wikipedia Politeness Corpus¶

wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))

# fiter utterances where binary variables were non zero 
wiki_corpus_binary = Corpus(utterances = [utt for utt in wiki_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])

# use Text Parser (parser has been pre loaded at the beginning )
wiki_corpus_binary = parser.transform(wiki_corpus_binary)

# use Politeness Transformer to annotate our data 
wiki_corpus_binary = ps.transform(wiki_corpus_binary, markers = True)

df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
display(df_wiki_binary.head())

Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus
2000/2178 utterances processed
2178/2178 utterances processed

Load Stack exchange corpus¶

stack_corpus = Corpus(download("stack-exchange-politeness-corpus"))
stack_corpus_binary = Corpus(utterances = [utt for utt in stack_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])

stack_corpus_binary = parser.transform(stack_corpus_binary)
stack_corpus_binary = ps.transform(stack_corpus_binary, markers = True)

df_stack_binary = stack_corpus_binary.get_utterances_dataframe()
display(df_stack_binary.head())

Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus
2000/3302 utterances processed
3302/3302 utterances processed

5ab. Using Built-In Bag of Words Transformer to build the model Wikipedia Politeness Corpus and Stanford Corpus¶

from convokit import BoWTransformer

bow_transformer = BoWTransformer(obj_type = "utterance", vector_name = 'bow')
bow_transformer2 = BoWTransformer(obj_type = "utterance", vector_name = 'bow')

Initializing default unigram CountVectorizer...Done.
Initializing default unigram CountVectorizer...Done.

bow_transformer.fit_transform(wiki_corpus_binary)
bow_transformer2.fit_transform(stack_corpus_binary)

<convokit.model.corpus.Corpus at 0x185bf752e08>

print(wiki_corpus_binary.vectors)
print(wiki_corpus_binary.vectors)

{'bow'}
{'bow'}

BoW Wiki Politeness Corpus¶

df_wiki_binary[0:2]

len(bow_transformer.get_vocabulary())

563

5ac. Structure of one of the vectors in the model¶

rand_utt = wiki_corpus_binary.random_utterance()
display(rand_utt.get_vector('bow', as_dataframe = True))

BoW Transformer contains the vector representation of the text.

bowvect_ = wiki_corpus_binary.get_vector_matrix('bow').to_dataframe()

bowvect_

binary_ = pd.DataFrame(binary_ )
binary_

df_bow_features = pd.concat([bowvect_ , binary_], axis = 1)
df_bow_features

X_trainbow, X_testbow, y_trainbow, y_testbow = split_data(df_bow_features, 0.90, df_bow_features.shape)

X_trainbow

lrbow_       =  LogisticRegression().fit(X_trainbow, y_trainbow.values.ravel())
mbbow_       =  MultinomialNB().fit(X_trainbow, y_trainbow.values.ravel())
svmbow_rbf   =  SVC(kernel = 'rbf',).fit(X_trainbow, y_trainbow.values.ravel())
svmbow_poly  =  SVC(kernel = 'poly').fit(X_trainbow, y_trainbow.values.ravel())
knnbow_      =  KNeighborsClassifier(n_neighbors = 3).fit(X_trainbow, y_trainbow.values.ravel())
dtreebow_    =  DecisionTreeClassifier(criterion = 'entropy').fit(X_trainbow, y_trainbow.values.ravel())
mlpbow_relu  =  MLPClassifier(hidden_layer_sizes = (30,30), activation = 'relu', max_iter = 500).fit(X_trainbow , y_trainbow.values.ravel())
mlpbow_tanh  =  MLPClassifier(hidden_layer_sizes = (30,30), activation = 'tanh', max_iter = 500).fit(X_trainbow , y_trainbow.values.ravel())
precpbow_    =  Perceptron().fit(X_trainbow, y_trainbow.values.ravel())

list_bow_classifier = [lrbow_, mbbow_, svmbow_rbf,svmbow_poly, knnbow_, dtreebow_,
                       mlpbow_relu, mlpbow_tanh, precpbow_ ]

count = 0
for model in list_bow_classifier:
    performPrediction(model, X_testbow, y_testbow, "Paired", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.791     0.821     0.806       106
           1      0.824     0.795     0.809       112

    accuracy                          0.807       218
   macro avg      0.807     0.808     0.807       218
weighted avg      0.808     0.807     0.807       218


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.792     0.792     0.792       106
           1      0.804     0.804     0.804       112

    accuracy                          0.798       218
   macro avg      0.798     0.798     0.798       218
weighted avg      0.798     0.798     0.798       218


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.781     0.840     0.809       106
           1      0.837     0.777     0.806       112

    accuracy                          0.807       218
   macro avg      0.809     0.808     0.807       218
weighted avg      0.809     0.807     0.807       218


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.608     0.906     0.727       106
           1      0.833     0.446     0.581       112

    accuracy                          0.670       218
   macro avg      0.720     0.676     0.654       218
weighted avg      0.724     0.670     0.652       218


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.511     0.877     0.646       106
           1      0.639     0.205     0.311       112

    accuracy                          0.532       218
   macro avg      0.575     0.541     0.478       218
weighted avg      0.577     0.532     0.474       218

5ad. Using Built-In Bag of Words Transformer to build the model Stanford Politeness Corpus¶

df_stack_binary[0:3]

stack_bowvect_ = stack_corpus_binary.get_vector_matrix('bow').to_dataframe()

stack_bowvect_

len(bow_transformer2.get_vocabulary())

717

df_bow_stack = pd.concat([stack_bowvect_ , df_stack_binary['meta.Binary'].astype("category")], axis = 1)
df_bow_stack[0:5]

df_bow_stack['meta.Binary'].replace({1:1, -1:0}, inplace = True)

df_bow_stack

X_trainbow, X_testbow, y_trainbow, y_testbow = split_data(df_bow_stack, 0.90, df_bow_stack.shape)

list_clf_stack_bow = generateModels(X_trainbow, y_trainbow)

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
              ]

count = 0
for model in list_clf_stack_bow:
    performPrediction(model, X_testbow, y_testbow, "Spectral", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.581     0.610     0.595       159
           1      0.622     0.593     0.607       172

    accuracy                          0.601       331
   macro avg      0.601     0.602     0.601       331
weighted avg      0.602     0.601     0.601       331


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.590     0.616     0.603       159
           1      0.630     0.605     0.617       172

    accuracy                          0.610       331
   macro avg      0.610     0.611     0.610       331
weighted avg      0.611     0.610     0.610       331


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.595     0.610     0.602       159
           1      0.631     0.616     0.624       172

    accuracy                          0.613       331
   macro avg      0.613     0.613     0.613       331
weighted avg      0.614     0.613     0.613       331


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.515     0.881     0.650       159
           1      0.678     0.233     0.346       172

    accuracy                          0.544       331
   macro avg      0.596     0.557     0.498       331
weighted avg      0.600     0.544     0.492       331


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.506     0.824     0.627       159
           1      0.611     0.256     0.361       172

    accuracy                          0.529       331
   macro avg      0.558     0.540     0.494       331
weighted avg      0.561     0.529     0.488       331

6. Combine BoW with linguistic features baseline¶

6a. Wiki¶

df_bow_features[0:3]

df_feature[0:3]

bow_and_pstrat_ = pd.concat([df_bow_features.drop(columns = ['meta.Binary']), df_feature], axis =1)

bow_and_pstrat_

Train the model¶

X_train_bow_polite, X_test_bow_polite, y_train_bow_polite, y_test_bow_polite = split_data(bow_and_pstrat_ , 0.90, 
                                                                          bow_and_pstrat_ .shape)
list_bow_polite = generateModels(X_train_bow_polite , y_train_bow_polite)

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
              ]

count = 0
for model in list_bow_polite:
    performPrediction(model, X_test_bow_polite, y_test_bow_polite, "CMRmap", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.819     0.811     0.815       106
           1      0.823     0.830     0.827       112

    accuracy                          0.821       218
   macro avg      0.821     0.821     0.821       218
weighted avg      0.821     0.821     0.821       218


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.808     0.792     0.800       106
           1      0.807     0.821     0.814       112

    accuracy                          0.807       218
   macro avg      0.807     0.807     0.807       218
weighted avg      0.807     0.807     0.807       218


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.827     0.811     0.819       106
           1      0.825     0.839     0.832       112

    accuracy                          0.826       218
   macro avg      0.826     0.825     0.825       218
weighted avg      0.826     0.826     0.826       218


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.658     0.925     0.769       106
           1      0.884     0.545     0.674       112

    accuracy                          0.729       218
   macro avg      0.771     0.735     0.721       218
weighted avg      0.774     0.729     0.720       218


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.582     0.868     0.697       106
           1      0.767     0.411     0.535       112

    accuracy                          0.633       218
   macro avg      0.674     0.639     0.616       218
weighted avg      0.677     0.633     0.614       218

df_bow_features

6b. Stack Exchange¶

df_bow_stack

df_standford_features

bow_and_pstrat_stack = pd.concat([df_bow_stack.drop(columns = ['meta.Binary']), df_standford_features], axis =1)
bow_and_pstrat_stack

X_train_bow_polite_2, X_test_bow_polite_2, y_train_bow_polite_2, y_test_bow_polite_2 = split_data(bow_and_pstrat_stack, 0.90, 
                                                                          bow_and_pstrat_stack.shape)
list_bow_polite_stack = generateModels(X_train_bow_polite_2 , y_train_bow_polite_2)

model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
              ]

X_train_bow_polite_2
X_test_bow_polite_2.shape

(331, 738)

count = 0
for model in list_bow_polite_stack:
    performPrediction(model, X_test_bow_polite_2, y_test_bow_polite_2, "coolwarm", model_names[count])
    count += 1


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.575     0.604     0.589       159
           1      0.616     0.587     0.601       172

    accuracy                          0.595       331
   macro avg      0.595     0.595     0.595       331
weighted avg      0.596     0.595     0.595       331


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.581     0.629     0.604       159
           1      0.629     0.581     0.604       172

    accuracy                          0.604       331
   macro avg      0.605     0.605     0.604       331
weighted avg      0.606     0.604     0.604       331


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.635     0.635     0.635       159
           1      0.663     0.663     0.663       172

    accuracy                          0.650       331
   macro avg      0.649     0.649     0.649       331
weighted avg      0.650     0.650     0.650       331


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.529     0.855     0.654       159
           1      0.689     0.297     0.415       172

    accuracy                          0.565       331
   macro avg      0.609     0.576     0.534       331
weighted avg      0.612     0.565     0.530       331


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.498     0.786     0.610       159
           1      0.575     0.267     0.365       172

    accuracy                          0.517       331
   macro avg      0.537     0.527     0.487       331
weighted avg      0.538     0.517     0.483       331

7 . TF-idf¶

Term frequency refers to the frequency of a term t in document d. The inverse document frequency is a measure of whether a term is common or rare. To analyze utterance text, convert text to its correspondinf tf-idf vector

from convokit import ColNormedTfidfTransformer

# lst= []
# id_list = []

# for utt in wiki_corpus_binary.iter_utterances():
#     lst.append(utt.text.lower())

# lst



# from sklearn.feature_extraction.text import TfidfTransformer
# from sklearn.feature_extraction.text import CountVectorizer
# from sklearn.pipeline import Pipeline
# vectorizer = CountVectorizer(stop_words='english',analyzer='word')
# document_term_matrix = vectorizer.fit_transform(lst)
# document_term_matrix.shape

# tfidf_transformer = TfidfTransformer()
# tf_idf = tfidf_transformer.fit_transform(document_term_matrix )

# kl = list(vectorizer.vocabulary_.keys())
# len(kl)

# df_feature



# x = pd.DataFrame(tf_idf.toarray() ,columns = kl )
# x


# print(list(x.columns))

7a. TF-IDF using Wiki Politeness Corpus¶

def load_wiki_politeness():
    ''' Function loads the corpus '''
    from convokit import TextParser, PolitenessStrategies
    
    parser = TextParser(verbosity = 2000)
    ps = PolitenessStrategies()
    
    wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))
    wiki_corpus_binary = Corpus(utterances = [utt for utt in wiki_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])

    wiki_corpus_binary = parser.transform(wiki_corpus_binary)
    wiki_corpus_binary = ps.transform(wiki_corpus_binary, markers = True)

    df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
    df_wiki_binary
    
    return wiki_corpus_binary, df_wiki_binary

#reload the corpus 

wiki_corpus_binary,df_wiki_binary = load_wiki_politeness()

Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus
2000/2178 utterances processed
2178/2178 utterances processed

df_wiki_binary[0:3]

parser = TextParser(verbosity = 2000)
ps = PolitenessStrategies()

wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))
wiki_corpus_binary = Corpus(utterances = [utt for utt in wiki_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])

wiki_corpus_binary = parser.transform(wiki_corpus_binary)
wiki_corpus_binary = ps.transform(wiki_corpus_binary, markers = True)

df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
df_wiki_binary.shape

Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus
2000/2178 utterances processed
2178/2178 utterances processed

(2178, 12)

7aa. Load TF-IDF Transformer provided by ConvoKit¶

from convokit.expected_context_framework import ColNormedTfidfTransformer

td_idf = ColNormedTfidfTransformer(input_field = 'text', output_field='col_normed_tfidf', model=None )

td_idf.fit_transform(wiki_corpus_binary)

<convokit.model.corpus.Corpus at 0x185994f2cc8>

wiki_corpus_binary.get_utterances_dataframe()[0:6]

#display tf-idf vector

rand_ = wiki_corpus_binary.random_utterance()
display(rand_.get_vector('col_normed_tfidf', as_dataframe = True))

wiki_corpus_binary.vectors

{'col_normed_tfidf'}

# display tf-idf matrix

td_idf_vector = wiki_corpus_binary.get_vector_matrix('col_normed_tfidf').to_dataframe()
td_idf_vector

# combine features (vocabulary) and outcome variable
pd.concat([td_idf_vector,binary_],axis=1)

7ab. Performing prediction task using Vector Classifier (ConvoKit Framework)¶

tf_idf_vect_clf     = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
                                     labeller=lambda utt: utt.meta['Binary'] == 1)

 
tf_idf_vect_clf_mb  = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf', clf = MultinomialNB(),
                                     labeller=lambda utt: utt.meta['Binary'] == 1)


tf_idf_vect_clf_dt  = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf', 
                                       clf = DecisionTreeClassifier(criterion = 'entropy'),
                                      labeller=lambda utt: utt.meta['Binary'] == 1)

tf_idf_vect_clf_knn = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf', 
                                       clf = KNeighborsClassifier(n_neighbors = 3),
                                       labeller=lambda utt: utt.meta['Binary'] == 1)


tf_idf_vect_clf_svc_rbf = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',  
                                           clf = SVC(kernel = 'rbf'),
                                           labeller=lambda utt: utt.meta['Binary'] == 1)

tf_idf_vect_clf_svc_poly = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',  
                                            clf = SVC(kernel = 'poly'),
                                            labeller=lambda utt: utt.meta['Binary'] == 1)


tf_idf_vect_clf_mlp_relu = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',  
                                       clf = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, activation='relu', random_state = 21),
                                     labeller=lambda utt: utt.meta['Binary'] == 1)

tf_idf_vect_clf_mlp_tanh = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',  
                                            clf = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,  activation='tanh', random_state = 22),
                                            labeller=lambda utt: utt.meta['Binary'] == 1)

tf_idf_vect_clf_precp = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',  
                                            clf = Perceptron(),
                                            labeller=lambda utt: utt.meta['Binary'] == 1)

Initialized default classification model (standard scaled logistic regression).

7ac. Perform Cross Validation using ConvoKit¶

scores_lr = tf_idf_vect_clf.evaluate_with_cv(wiki_corpus_binary)
print(scores_lr)

Running a cross-validated evaluation...Done.
[0.74770642 0.72477064 0.76605505 0.75172414 0.74482759]

scores_mb = tf_idf_vect_clf_mb.evaluate_with_cv(wiki_corpus_binary)
print(scores_mb)

Running a cross-validated evaluation...Done.
[0.68577982 0.70183486 0.69954128 0.70804598 0.71724138]

scores_dt = tf_idf_vect_clf_dt.evaluate_with_cv(wiki_corpus_binary)

Running a cross-validated evaluation...Done.

scores_knn =  tf_idf_vect_clf_knn.evaluate_with_cv(wiki_corpus_binary)

Running a cross-validated evaluation...Done.

scores_svc_1 = tf_idf_vect_clf_svc_rbf.evaluate_with_cv(wiki_corpus_binary)

Running a cross-validated evaluation...Done.

scores_svc_2 = tf_idf_vect_clf_svc_poly.evaluate_with_cv(wiki_corpus_binary) 
print(scores_svc_2)

scores_mlp_1 = tf_idf_vect_clf_mlp_relu.evaluate_with_cv(wiki_corpus_binary)

scores_mlp_2 =  tf_idf_vect_clf_mlp_tanh.evaluate_with_cv(wiki_corpus_binary)

scores_precp = tf_idf_vect_clf_precp.evaluate_with_cv(wiki_corpus_binary)

Running a cross-validated evaluation...Done.
[0.53211009 0.50229358 0.46330275 0.46896552 0.53563218]
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.

7ad. Display mean accuracy score for CV¶

score_ = [np.mean(scores_lr), np.mean(scores_mb), np.mean(scores_dt),
          np.mean(scores_knn),np.mean(scores_svc_1),np.mean(scores_svc_2),
          np.mean(scores_mlp_1),np.mean(scores_mlp_2),np.mean(scores_precp)]

frame = pd.DataFrame({ 'Cross Validation Mean score ': score_}, index = ['Standard Logistic Regression',
                                                                        'Mutinomial Naive Bayes', 'Decision Tree',
                                                                        'KNeighbors Classifiers','SVC RBF','SVC Poly',
                                                                         'MLP (relu)','MLP(tanh)','Perceptron '])
frame

7ae. Split data into training and testing set. Call the method defined above¶

X_train_tf, X_test_tf, y_train_tf, y_test_tf = split_data(pd.concat([td_idf_vector,binary_],axis=1), 0.90, pd.concat([td_idf_vector,binary_],axis=1).shape)

#Generate classifiers and train them using training data

tf_idf_models = generateModels(X_train_tf,y_train_tf)

7af. Test the models using remaining observations¶

model_names = ["Logisitc Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
              ]

count = 0
for model in tf_idf_models:
    performPrediction(model, X_test_tf, y_test_tf, "Paired", model_names[count])
    count += 1


----------------Confusion Matrix for Logisitc Regression  Classifier--------------------


	Classification Report for Logisitc Regression  Classifier

              precision    recall  f1-score   support

           0      0.780     0.868     0.821       106
           1      0.860     0.768     0.811       112

    accuracy                          0.817       218
   macro avg      0.820     0.818     0.816       218
weighted avg      0.821     0.817     0.816       218


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.808     0.594     0.685       106
           1      0.693     0.866     0.770       112

    accuracy                          0.734       218
   macro avg      0.750     0.730     0.727       218
weighted avg      0.749     0.734     0.728       218


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.742     0.679     0.709       106
           1      0.719     0.777     0.747       112

    accuracy                          0.729       218
   macro avg      0.731     0.728     0.728       218
weighted avg      0.730     0.729     0.729       218


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.000     0.000     0.000       106
           1      0.514     1.000     0.679       112

    accuracy                          0.514       218
   macro avg      0.257     0.500     0.339       218
weighted avg      0.264     0.514     0.349       218


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      1.000     0.038     0.073       106
           1      0.523     1.000     0.687       112

    accuracy                          0.532       218
   macro avg      0.762     0.519     0.380       218
weighted avg      0.755     0.532     0.388       218

7ag. Test and Train data using ConvoKit¶

# Spilt data
train_convos, test_convos = train_test_split(list(wiki_corpus_binary.iter_utterances())
                                                  , test_size=0.1, shuffle=False)


# Iterate over utterances using their corresponding id's and mark them as train and test
for utt in train_convos:
    utt.meta['train_test_type'] = 'train'
    
for utt in test_convos:
    utt.meta['train_test_type'] = 'test'

# Fit the model with training set labels only. Using a lambda function filter such training samples    
tf_idf_vect_clf.fit(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')

#Using a lambda function predict the outcome variable for only test set   
tf_idf_vect_clf.transform(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')

# Print classification report
print("*********************** Classification Report (Standard Logisitic Regression)*************************")
print(tf_idf_vect_clf.classification_report(wiki_corpus_binary, 
                                            selector=lambda utt: utt.meta['train_test_type'] == 'test'))

*********************** Classification Report (Standard Logisitic Regression)*************************
              precision    recall  f1-score   support

       False       0.84      0.79      0.82       106
        True       0.81      0.86      0.83       112

    accuracy                           0.83       218
   macro avg       0.83      0.82      0.83       218
weighted avg       0.83      0.83      0.83       218

tf_idf_vect_clf_mb.fit(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
tf_idf_vect_clf_mb.transform(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
print("*********************** Classification Report (Naive Bayes)*************************")
print(tf_idf_vect_clf_mb.classification_report(wiki_corpus_binary, 
                                            selector=lambda utt: utt.meta['train_test_type'] == 'test'))

*********************** Classification Report (Naive Bayes)*************************
              precision    recall  f1-score   support

       False       0.81      0.59      0.68       106
        True       0.69      0.87      0.77       112

    accuracy                           0.73       218
   macro avg       0.75      0.73      0.73       218
weighted avg       0.75      0.73      0.73       218

tf_idf_vect_clf_dt.fit(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
tf_idf_vect_clf_dt.transform(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')

print(tf_idf_vect_clf_dt.classification_report(wiki_corpus_binary, 
                                            selector=lambda utt: utt.meta['train_test_type'] == 'test'))

              precision    recall  f1-score   support

       False       0.69      0.73      0.71       106
        True       0.73      0.69      0.71       112

    accuracy                           0.71       218
   macro avg       0.71      0.71      0.71       218
weighted avg       0.71      0.71      0.71       218

Summary Statistics¶

tf_idf_vect_clf.summarize(wiki_corpus_binary).head(5)

tf_idf_vect_clf_mb.summarize(wiki_corpus_binary).head(5)

tf_idf_vect_clf_dt.summarize(wiki_corpus_binary).head(10)

7b. TF-IDF using Stack Exchange Corpus¶

To print run TF-IDF on Stack Exchange run the following function. This function uses inbuilt ConvoKit Vector Classifier which takes vector as input and by default runs Standard Logistic Regression Classifier which can changed to run different classifiers by altering the parameters. However the function uses extremely slow . If cross Validation scores are not required simply use the next function instead i.e. stackExchangeTFIDF(). All the metrics are the same irrespective of the method used

def stackTFIDFVectoriConvo():
    
    from convokit import TextParser, PolitenessStrategies
    from convokit.expected_context_framework import ColNormedTfidfTransformer
    
    # Call both Text Parser and Politeness Strategies Tranformer
    parser = TextParser(verbosity = 3000)
    ps = PolitenessStrategies()
    
    #Load corpus and filter out utterances with class label of 0
    _corpus = Corpus(download("stack-exchange-politeness-corpus"))
    _corpus_binary = Corpus(utterances = [utt for utt in _corpus.iter_utterances() if utt.meta['Binary']!= 0 ])

    # tranform the corpus using the transformers
    _corpus_binary = parser.transform(_corpus_binary)
    _corpus_binary = ps.transform(_corpus_binary, markers = True)

    df_binary      = _corpus_binary.get_utterances_dataframe()
    display(df_binary.head())
    
    # Use tf-idf transformer to convert utterance text to numeric data using term frequency and inverse frequency
    # in a document
    td_idf = ColNormedTfidfTransformer(input_field = 'text', output_field='col_normed_tfidf', model=None )
    
    td_idf.fit_transform(_corpus_binary)
    
    # Extract the matrix produced by tf-idf tranformer
    td_idf_vector = _corpus_binary.get_vector_matrix('col_normed_tfidf').to_dataframe()
    display(td_idf_vector.head()) 

    model_names = ["Logisitc Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"]
    
    # Use ML/DL classifiers
    lr_clf        =  LogisticRegression()
    mb_clf        =  MultinomialNB()
    svm_clf_rbf   =  SVC(kernel = 'rbf', probability= True)
    svm_clf_poly  =  SVC(kernel = 'poly',probability= True )
    knn_clf       =  KNeighborsClassifier(n_neighbors = 3, weights='uniform', 
                                          metric='minkowski')
    dtree_clf     =  DecisionTreeClassifier(criterion ="entropy")
    mlp_clf_relu  =  MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='relu', random_state = 21)
    mlp_clf_tanh  =  MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='tanh', random_state = 22)
#     precp_clf     =  Perceptron()
    
    clf_models = [lr_clf, mb_clf , svm_clf_rbf, svm_clf_poly, knn_clf, dtree_clf, mlp_clf_relu, mlp_clf_tanh]
    
    
    # Call the vector classifier of ConvoKit frame work 
    tf_idf_vect_clf_std     = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
                                     labeller=lambda utt: utt.meta['Binary'] == 1)
    
    mytf_idf_lst= []
    
    # Call Vector Classifier for all the models. Using a loop instead, create model object and store it in a list 
    
    for models in clf_models:
        tf_idf_vect_clf = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf', clf = models,
                                     labeller=lambda utt: utt.meta['Binary'] == 1)
        mytf_idf_lst.append(tf_idf_vect_clf)
        
    score_list = list() 

    score_std = tf_idf_vect_clf_std.evaluate_with_cv(_corpus_binary) 
    
    # Calculate CV scores for all of the  models

    for i in mytf_idf_lst:
        score_ = np.mean(i.evaluate_with_cv(_corpus_binary))
        score_list.append(score_)

    score_list.insert(0,np.mean(score_std))   

    frame = pd.DataFrame({ 'Cross Validation Mean score ': score_list}, index = model_names)
    display(frame)
    
    
    # As done earlier split the data into training and testing test using utterance ids
    train_convos, test_convos = train_test_split(list(_corpus_binary.iter_utterances())
                                                  , test_size=0.1, shuffle=False)
    for utt in train_convos:
        utt.meta['train_test_type'] = 'train'
        
        
    for utt in test_convos:
        utt.meta['train_test_type'] = 'test'
    
    tf_idf_vect_clf_std.fit(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
    tf_idf_vect_clf_std.transform(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')


    print("Classification Report for Standard Logistic Regression ")
    print(tf_idf_vect_clf_std.classification_report(_corpus_binary, 
                                            selector=lambda utt: utt.meta['train_test_type'] == 'test'))
    
    # Print classification report for all the models generated and stored in a list
    for i , model in enumerate(mytf_idf_lst):
        model.fit(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
        model.transform(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
        name = model_names[i]
        print(model)
        print(f"*******************Classification Report for {name}*********************************")
        print(model.classification_report(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test'))

stackTFIDFVectoriConvo()

Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus
3000/3302 utterances processed
3302/3302 utterances processed

Initialized default classification model (standard scaled logistic regression).
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.
Running a cross-validated evaluation...Done.

Classification Report for Standard Logistic Regression 
              precision    recall  f1-score   support

       False       0.56      0.55      0.56       159
        True       0.59      0.61      0.60       172

    accuracy                           0.58       331
   macro avg       0.58      0.58      0.58       331
weighted avg       0.58      0.58      0.58       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B46E3A48>
*******************Classification Report for Logisitc Regression *********************************
              precision    recall  f1-score   support

       False       0.53      0.65      0.59       159
        True       0.59      0.47      0.52       172

    accuracy                           0.56       331
   macro avg       0.56      0.56      0.55       331
weighted avg       0.56      0.56      0.55       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185DD0F0D88>
*******************Classification Report for Naive Bayes*********************************
              precision    recall  f1-score   support

       False       0.50      0.57      0.53       159
        True       0.55      0.48      0.51       172

    accuracy                           0.52       331
   macro avg       0.52      0.52      0.52       331
weighted avg       0.53      0.52      0.52       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861A8C8>
*******************Classification Report for Support Vector Machine (Kernel= (RBF))*********************************
              precision    recall  f1-score   support

       False       0.50      0.58      0.54       159
        True       0.54      0.45      0.49       172

    accuracy                           0.52       331
   macro avg       0.52      0.52      0.52       331
weighted avg       0.52      0.52      0.51       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861AAC8>
*******************Classification Report for Support Vector Machine (Kernel = Polynomial)*********************************
              precision    recall  f1-score   support

       False       0.48      1.00      0.65       159
        True       0.00      0.00      0.00       172

    accuracy                           0.48       331
   macro avg       0.24      0.50      0.32       331
weighted avg       0.23      0.48      0.31       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861AA48>
*******************Classification Report for K-Nearest Neighbour*********************************
              precision    recall  f1-score   support

       False       0.48      1.00      0.65       159
        True       0.00      0.00      0.00       172

    accuracy                           0.48       331
   macro avg       0.24      0.50      0.32       331
weighted avg       0.23      0.48      0.31       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861AA88>
*******************Classification Report for Decision Tree*********************************
              precision    recall  f1-score   support

       False       0.53      0.60      0.56       159
        True       0.58      0.50      0.54       172

    accuracy                           0.55       331
   macro avg       0.55      0.55      0.55       331
weighted avg       0.55      0.55      0.55       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185DD0F3EC8>
*******************Classification Report for MLP(Multi-Layer Preceptron(Relu))*********************************
              precision    recall  f1-score   support

       False       0.51      0.57      0.54       159
        True       0.55      0.48      0.51       172

    accuracy                           0.53       331
   macro avg       0.53      0.53      0.53       331
weighted avg       0.53      0.53      0.52       331

<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185DD0F3F08>
*******************Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent))*********************************
              precision    recall  f1-score   support

       False       0.51      0.61      0.56       159
        True       0.56      0.47      0.51       172

    accuracy                           0.53       331
   macro avg       0.54      0.54      0.53       331
weighted avg       0.54      0.53      0.53       331

def stackExchangeTFIDF():
    
    from convokit import TextParser, PolitenessStrategies
    from convokit.expected_context_framework import ColNormedTfidfTransformer
    
    # Call both Text Parser and Politeness Strategies Tranformer
    parser = TextParser(verbosity = 3000)
    ps = PolitenessStrategies()
    
    #Load corpus and filter out utterances with class label of 0
    _corpus = Corpus(download("stack-exchange-politeness-corpus"))
    _corpus_binary = Corpus(utterances = [utt for utt in _corpus.iter_utterances() if utt.meta['Binary']!= 0 ])

    # tranform the corpus using the transformers
    _corpus_binary = parser.transform(_corpus_binary)
    _corpus_binary = ps.transform(_corpus_binary, markers = True)

    df_binary      =  _corpus_binary.get_utterances_dataframe()
    display(df_binary.head())
    
    # Use tf-idf transformer to convert utterance text to numeric data using term frequency and inverse frequency
    # in a document
    td_idf = ColNormedTfidfTransformer(input_field = 'text', output_field='col_normed_tfidf', model=None )
    
    td_idf.fit_transform(_corpus_binary)
    
    # Extract the matrix produced by tf-idf tranformer. Remap 0:ImPolite 1:Polite instead of -1 and 1 respectively
    td_idf_vector  = _corpus_binary.get_vector_matrix('col_normed_tfidf').to_dataframe()
    binary_        =  df_binary['meta.Binary'].astype("category")
    changedicti_   =  {-1 : 0 , 1 : 1 }
    binary_.replace(changedicti_ , inplace = True)
    display(td_idf_vector.head()) 

    model_names = ["Logisitc Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
                "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"]
    

    lr_clf        =  LogisticRegression()
    mb_clf        =  MultinomialNB()
    svm_clf_rbf   =  SVC(kernel = 'rbf', probability= True)
    svm_clf_poly  =  SVC(kernel = 'poly', probability= True )
    knn_clf       =  KNeighborsClassifier(n_neighbors = 3, weights='uniform', 
                                          metric='minkowski')
    dtree_clf     =  DecisionTreeClassifier(criterion ="entropy")
    mlp_clf_relu  =  MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='relu', random_state = 21)
    mlp_clf_tanh  =  MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, 
                             activation='tanh', random_state = 22)
    precp_clf     =  Perceptron()
    
    clf_models = [mb_clf , svm_clf_rbf, svm_clf_poly, knn_clf, dtree_clf, mlp_clf_relu, mlp_clf_tanh, precp_clf]
    
    
    # Split data using spilt_data() defined above
    X_train_tf, X_test_tf, y_train_tf, y_test_tf = split_data(pd.concat([td_idf_vector, binary_],axis=1), 
                                                              0.90, pd.concat([td_idf_vector,binary_],axis=1).shape)
    #Fit models
    tf_idf_models = generateModels(X_train_tf,y_train_tf)
    
    count = 0
    
    for model in tf_idf_models:
        performPrediction(model, X_test_tf, y_test_tf, "Paired", model_names[count])
        count += 1

stackExchangeTFIDF()

Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus
3000/3302 utterances processed
3302/3302 utterances processed


----------------Confusion Matrix for Logisitc Regression  Classifier--------------------


	Classification Report for Logisitc Regression  Classifier

              precision    recall  f1-score   support

           0      0.531     0.654     0.586       159
           1      0.593     0.465     0.521       172

    accuracy                          0.556       331
   macro avg      0.562     0.560     0.554       331
weighted avg      0.563     0.556     0.552       331


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.503     0.566     0.533       159
           1      0.546     0.483     0.512       172

    accuracy                          0.523       331
   macro avg      0.524     0.524     0.522       331
weighted avg      0.525     0.523     0.522       331


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.497     0.585     0.538       159
           1      0.542     0.453     0.494       172

    accuracy                          0.517       331
   macro avg      0.519     0.519     0.516       331
weighted avg      0.520     0.517     0.515       331


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.480     1.000     0.649       159
           1      0.000     0.000     0.000       172

    accuracy                          0.480       331
   macro avg      0.240     0.500     0.324       331
weighted avg      0.231     0.480     0.312       331


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------

8. Future Works for WIKI- TALK Pages Corpus¶

Using the above Linguistic Marker models for Wikipedia we can annotate our Wiki Talk Corpus for being polite or not. As Wiki Talk Corpus contains 400,0000 utterances annotating each and every utterance is not possible .As mentioned in ('research paper') we can use Linguistic marker model built on 2718 utterances to annotate the data. We can then combine 2718 utterances obtained from wiki with 3002 utterances and build a better model. However for this experiment Wiki Politeness Linguistic Marker Model will be used

8a. Semi Supervised Learning¶

As we dont have gold standard labels available for our dataset Techniques such semi supervised learning in such scenrios. First predict the labels using Wikipedia Politeness Corpus. The use those labels as gold standard labels to built models on Wiki-Talk Corpus and evaluate performance. For this short demo only Naive Bayes classifier will be used to predict labels for utterance level information in Wiki - Talk Corpus

def load_wiki_corpus_full():
    
    from convokit import TextParser, PolitenessStrategies
    
    parser = TextParser(verbosity = 10000)
    ps = PolitenessStrategies()
    
    # As Corpus is huge use only first 50,000 utterances
    wiki_corpus = Corpus(download('wiki-corpus'), utterance_end_index = 50000)

    wiki_corpus= parser.transform(wiki_corpus)
    wiki_corpus = ps.transform(wiki_corpus, markers = True)

    df_wiki_ = wiki_corpus.get_utterances_dataframe()
    
    return wiki_corpus, df_wiki_

wiki_full , df_wiki_utt = load_wiki_corpus_full()

Downloading wiki-corpus to C:\Users\pulki\capstone project\march 11th 2021 politeness\wiki-corpus
Downloading wiki-corpus from http://zissou.infosci.cornell.edu/convokit/datasets/wiki-corpus/wiki-corpus.zip (238.4MB)... Done
10000/50001 utterances processed
20000/50001 utterances processed
30000/50001 utterances processed
40000/50001 utterances processed
50000/50001 utterances processed
50001/50001 utterances processed

df_wiki_utt[0:3]

Speaker level Information Wiki Talk Corpus¶

wiki_full.get_speakers_dataframe()

Build Linguistic Marker model with 21 features¶

utterance_ids = wiki_full.get_utterance_ids()

rows = list()

for ids in utterance_ids:
    rows.append(wiki_full.get_utterance(ids).meta['politeness_strategies'])
    
_wiki_features = pd.DataFrame(rows, index = utterance_ids)

_wiki_features

Predict the gold standard labels

gold_labels = mb_clf.predict(_wiki_features)

gold_labl_series = pd.Series(gold_labels , index = utterance_ids, name = 'meta.Binary')

# get value counts for each class labels

gold_labl_series.value_counts()

1    40137
0     9864
Name: meta.Binary, dtype: int64

df_  = pd.concat([_wiki_features, gold_labl_series] , axis = 1)

df_

The information above shows that the for Polite class label we have 40137 and for Impolite class label we have 9864 observations. As the classed are not balanced with observations associated to Impolite Class account only for 20% the model will be a weak classifier. We will try to address this issue using SMOTE analysis . Refer [3]

(9864/(40137+9864))*100

19.72760544789104

(40137/(40137+9864))*100

80.27239455210896

9864/40137

0.24575827789819868

8b. Build Supervised Learning Models¶

Now use these to build a model and train various classifiers on it

X_train_w, X_test_w, y_train_w, y_test_w = split_data(df_, 0.80, df_.shape)

models_ = generateModels(X_train_w, y_train_w)

def getClassifiStats(models , X_test, y_test):
    
    model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
               "Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
               "Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
                "Perceptron"
              ]

    count = 0
    for i in models:
        performPrediction(i , X_test, y_test, "Paired", model_names[count])
        count += 1

getClassifiStats(models_ , X_test_w, y_test_w)


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.983     0.983     0.983      1923
           1      0.996     0.996     0.996      8078

    accuracy                          0.994     10001
   macro avg      0.990     0.990     0.990     10001
weighted avg      0.994     0.994     0.994     10001


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.884     0.261     0.403      1923
           1      0.849     0.992     0.915      8078

    accuracy                          0.851     10001
   macro avg      0.867     0.626     0.659     10001
weighted avg      0.856     0.851     0.817     10001


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.997     0.995     0.996      1923
           1      0.999     0.999     0.999      8078

    accuracy                          0.999     10001
   macro avg      0.998     0.997     0.998     10001
weighted avg      0.998     0.999     0.998     10001


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.986     0.996     0.991      1923
           1      0.999     0.997     0.998      8078

    accuracy                          0.997     10001
   macro avg      0.993     0.996     0.994     10001
weighted avg      0.997     0.997     0.997     10001


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.990     0.981     0.985      1923
           1      0.995     0.998     0.996      8078

    accuracy                          0.994     10001
   macro avg      0.992     0.989     0.991     10001
weighted avg      0.994     0.994     0.994     10001

SMOTE Analysis¶

SMOTE is an oversampling technique where the synthetic samples are generated for the minority class. There are various variations of this technique. We however used the most basic version of it . For More information refer([3]) and https://www.analyticsvidhya.com/blog/2020/10/overcoming-class-imbalance-using-smote-techniques/

pip install imblearn

Requirement already satisfied: imblearn in c:\users\pulki\anaconda3\lib\site-packages (0.0)
Requirement already satisfied: imbalanced-learn in c:\users\pulki\anaconda3\lib\site-packages (from imblearn) (0.9.0)
Requirement already satisfied: scikit-learn>=1.0.1 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (1.0.2)
Requirement already satisfied: numpy>=1.14.6 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (1.21.6)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (3.1.0)
Requirement already satisfied: joblib>=0.11 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (0.14.1)
Requirement already satisfied: scipy>=1.1.0 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (1.4.1)
Note: you may need to restart the kernel to use updated packages.

print("Before OverSampling, counts of labels : {}".format(y_train_w.value_counts()))

Before OverSampling, counts of labels : meta.Binary
1              32059
0               7941
dtype: int64

from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE

sm = SMOTE(random_state = 2)
X_train_res, y_train_res = sm.fit_resample(X_train_w, y_train_w)

print('Before OverSampling, the shape of train_X: {}'.format(X_train_w.shape))
print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))
print('After OverSampling, the shape of train_y: {} \n'.format(y_train_res.shape))

Before OverSampling, the shape of train_X: (40000, 21)
After OverSampling, the shape of train_X: (64118, 21)
After OverSampling, the shape of train_y: (64118, 1)

print("After OverSampling, counts of labels : {}".format(y_train_res.value_counts()))

After OverSampling, counts of labels : meta.Binary
0              32059
1              32059
dtype: int64

models_SMOTE = generateModels(X_train_res , y_train_res)

getClassifiStats(models_SMOTE , X_test_w, y_test_w)


----------------Confusion Matrix for Logistic Regression  Classifier--------------------


	Classification Report for Logistic Regression  Classifier

              precision    recall  f1-score   support

           0      0.927     0.992     0.958      1923
           1      0.998     0.981     0.990      8078

    accuracy                          0.983     10001
   macro avg      0.962     0.986     0.974     10001
weighted avg      0.984     0.983     0.984     10001


----------------Confusion Matrix for Naive Bayes Classifier--------------------


	Classification Report for Naive Bayes Classifier

              precision    recall  f1-score   support

           0      0.753     0.966     0.846      1923
           1      0.991     0.924     0.957      8078

    accuracy                          0.932     10001
   macro avg      0.872     0.945     0.901     10001
weighted avg      0.945     0.932     0.935     10001


----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier

              precision    recall  f1-score   support

           0      0.997     0.995     0.996      1923
           1      0.999     0.999     0.999      8078

    accuracy                          0.998     10001
   macro avg      0.998     0.997     0.997     10001
weighted avg      0.998     0.998     0.998     10001


----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------


	Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier

              precision    recall  f1-score   support

           0      0.996     0.998     0.997      1923
           1      1.000     0.999     0.999      8078

    accuracy                          0.999     10001
   macro avg      0.998     0.998     0.998     10001
weighted avg      0.999     0.999     0.999     10001


----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------


	Classification Report for K-Nearest Neighbour Classifier

              precision    recall  f1-score   support

           0      0.988     0.981     0.985      1923
           1      0.996     0.997     0.996      8078

    accuracy                          0.994     10001
   macro avg      0.992     0.989     0.991     10001
weighted avg      0.994     0.994     0.994     10001

8c. Wiki Corpus Coordination Analysis¶

The Conversational Data Collected cannot only be used to predict politeness outcome. It can be used to answer question such as "DO admins communicate with users more than non admins ?" or DO users communicate more to non admin or admin. This can be answered using Coordination scores provided by Coordination Transformer, a Transformer provided by ConvoKit.

# Load arc dependencies as it will include information such as dependency structure  
wiki_full.load_info('utterance',['arcs_censored'])

# change arcs to list of arc_censored as the Coordination doesnt accept list as input. Input has to be string

from convokit.text_processing import TextProcessor

join_arcs = TextProcessor(input_field='arcs_censored', output_field='arcs',
                     proc_fn=lambda sents: '\n'.join(sents))

wiki_full = join_arcs.transform(wiki_full)

FIT Corpus to learn coordination pattern¶

coord = convokit.Coordination()
coord.fit(wiki_full)

Notice how Linguistic features have been added to the dataset¶

Coordination scores get calculated using liwc-categories. For instance an utterance has auxverb, conjuction, preposition etc which is associated to a particular speaker. Suppose this speaker uses a lot of these morphological features. These features can be characaterstic of a speaker. These morphological features aggregated and key characteristic features specific to particular users can be identified. For example, speaker 'A' ,uses of auxverb , pronouns or other linguistic markers, which happens to be an admin. Speaker b,c and d also have this in common. By using aggregated scores we can conclude that admins use a lot of auxverb, pronouns etc in there replies or text

wiki_full.get_utterances_dataframe()

c = coord.summarize(wiki_full)
c

{Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '7098'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'CycloneGU'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.1428571428571429,
  'adverb': 0.0,
  'ppron': 0.11904761904761907,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.09523809523809523},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '0'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'EA210269'}): {'article': 0.0,
  'auxverb': -0.015238095238095273,
  'conj': -0.03809523809523818,
  'adverb': -0.01090909090909098,
  'ppron': 0.023636363636363678,
  'ipron': -0.025263157894736876,
  'preps': 0.0,
  'quant': -0.06222222222222218},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '6601'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Marcbela'}): {'article': 0.08181818181818179,
  'auxverb': 0.054545454545454564,
  'conj': 0.06060606060606061,
  'adverb': 0.17045454545454547,
  'ppron': 0.0,
  'ipron': 0.04040404040404039,
  'preps': 0.0,
  'quant': 0.1515151515151515},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '15258'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'SynergyStar'}): {'article': -0.020000000000000018,
  'auxverb': 0.0,
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  'adverb': -0.08000000000000007,
  'ppron': -0.02285714285714291,
  'ipron': -0.10000000000000003,
  'preps': -0.01090909090909098,
  'quant': -0.1333333333333333},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '1'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Gerardw'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '21783'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Synergy'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '41292'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Realkyhick'}): {'article': -0.005681818181818232,
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  'quant': -0.08181818181818179},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '39456'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Hrafn'}): {'article': -0.050000000000000044,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '232528'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Courcelles'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': 'unknown'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': '24.130.14.173'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '100508'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'AnemoneProjectors'}): {'article': 0.2666666666666666,
  'auxverb': 0.15000000000000002,
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  'adverb': 0.09999999999999998,
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  'quant': 0.2666666666666666},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '11639'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Julia Rossi'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '126074'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Plastikspork'}): {'article': -0.09999999999999998,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '17266'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Arjayay'}): {'article': 0.15000000000000002,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '66512'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Scorpion0422'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '58346'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Chrisjnelson'}): {'article': -0.0292397660818714,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '1127'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'DanaUllman'}): {'auxverb': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '94317'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Bongwarrior'}): {'article': 0.15000000000000002,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '38301'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Snek01'}): {'article': -0.03532608695652173,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': 'unknown'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Peregrine_Fisher'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '44937'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Tagishsimon'}): {'article': 0.16666666666666663,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '66791'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Wehwalt'}): {'article': 0.0036472859901308663,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '19543'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'NickOrnstein'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '29884'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Aleksandr Grigoryev'}): {'article': -0.08333333333333337,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '7203'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Ardenn'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '23970'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Mets501'}): {'article': -0.07792207792207795,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '2756'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Mr. Neutron'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '7614'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Drutt'}): {'article': -0.08333333333333337,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '26998'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Cunard'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '23015'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Literaturegeek'}): {'article': -0.08333333333333337,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '16429'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'RashersTierney'}): {'auxverb': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '1200'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Zeeboid'}): {'article': 0.08333333333333331,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '20476'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Andre Engels'}): {'article': 0.032467532467532534,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '20232'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Niteshift36'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '19482'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'BrendelSignature'}): {'article': 0.017343318880567615,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': 'unknown'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'culverin'}): {'article': -0.12987012987012986,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '193945'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Bhadani'}): {'article': -0.09999999999999998,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '8704'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Jaysweet'}): {'auxverb': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '3012'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Kseferovic'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '0'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'ChrisO'}): {'article': 0.0,
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 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '18313'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Grandmaster'}): {'article': 0.0,
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  'adverb': 0.16666666666666663,
  'ppron': 0.25,
  'ipron': 0.16666666666666663,
  'preps': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '6329'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'DeCausa'}): {'article': 0.034055727554179516,
  'auxverb': -0.0029239766081871066,
  'conj': 0.040935672514619936,
  'adverb': 0.11961722488038273,
  'ppron': 0.04024767801857587,
  'ipron': -0.018575851393188847,
  'preps': -0.011695906432748537,
  'quant': 0.08502024291497978},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '37246'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'DIREKTOR'}): {'auxverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '2661'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Xino'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '42028'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'FayssalF'}): {'article': 0.008928571428571397,
  'auxverb': 0.044642857142857095,
  'conj': 0.004807692307692291,
  'adverb': 0.011363636363636354,
  'ppron': 0.09615384615384615,
  'ipron': 0.22916666666666663,
  'preps': 0.050000000000000044,
  'quant': 0.09999999999999998},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '9314'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Father Goose'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': -0.033333333333333326,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': -0.050000000000000044},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '2149'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Taalo'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': -0.033333333333333326,
  'adverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '3835'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Spidern'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '117172'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Juliancolton'}): {'article': 0.0,
  'auxverb': -0.08333333333333337,
  'conj': -0.08333333333333337,
  'adverb': -0.16666666666666669,
  'ppron': 0.0,
  'ipron': -0.08333333333333337,
  'preps': 0.0,
  'quant': -0.16666666666666669},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '6280'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Billlion'}): {'article': -0.015873015873015872,
  'auxverb': -0.04166666666666663,
  'conj': 0.0,
  'adverb': 0.06666666666666671,
  'ppron': -0.09523809523809523,
  'ipron': -0.12698412698412703,
  'preps': -0.08333333333333331,
  'quant': 0.06349206349206349},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '18949'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Nascar1996'}): {'article': -0.0008928571428571397,
  'auxverb': -0.001680672268907557,
  'conj': 0.014814814814814725,
  'adverb': -0.0071428571428572285,
  'ppron': 0.02857142857142858,
  'ipron': 0.0071428571428571175,
  'preps': -0.005882352941176561,
  'quant': 0.002955665024630627},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '4733'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'ROxBo'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '6144'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Ebizur'}): {'article': -0.038277511961722466,
  'auxverb': 0.052631578947368474,
  'conj': 0.015037593984962405,
  'adverb': 0.004048582995951455,
  'ppron': 0.03383458646616544,
  'ipron': 0.052631578947368474,
  'preps': 0.0,
  'quant': -0.014035087719298178},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '20858'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Fainites'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': 0.16666666666666663,
  'ppron': 0.0,
  'ipron': -0.08333333333333337,
  'preps': -0.16666666666666669},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '60600'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Durova'}): {'article': 0.05641025641025643,
  'auxverb': 0.0,
  'conj': 0.1333333333333333,
  'adverb': -0.01025641025641022,
  'ppron': -0.01025641025641022,
  'ipron': 0.06666666666666665,
  'preps': 0.0,
  'quant': 0.033333333333333326},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '288705'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Skier Dude'}): {'article': 0.02564102564102566,
  'auxverb': -0.009523809523809601,
  'conj': 0.10000000000000009,
  'adverb': 0.06060606060606066,
  'ppron': -0.023809523809523725,
  'ipron': 0.03589743589743599,
  'preps': -0.01904761904761898,
  'quant': -0.012121212121212144},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '2305'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Iñaki LL'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.1428571428571429,
  'adverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.23809523809523808},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '13927'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Davodd'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': 0.0,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.0},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '25928'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Emeraude'}): {'article': 0.0,
  'auxverb': -0.050000000000000044,
  'conj': -0.15000000000000002,
  'adverb': -0.050000000000000044,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': -0.15000000000000002},
 Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '20809'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Trovatore'}): {'article': 0.0,
  'auxverb': 0.0,
  'conj': 0.0,
  'adverb': 0.09722222222222221,
  'ppron': 0.0,
  'ipron': 0.0,
  'preps': 0.0,
  'quant': 0.08333333333333337},
 ...}

wiki_full.get_speakers_dataframe()

Transfor Corpus to show coordination scores¶

Aggregated corresponding to each of the speakers will be displayed

coord.transform(wiki_full)

<convokit.model.corpus.Corpus at 0x185f14b09c8>

Note: Some Scores might be missing

speaker_df  = wiki_full.get_speakers_dataframe()
speaker_df

Filter out missing scores¶

speaker_df[speaker_df['meta.coord'].notna()]

Find Coordination between Speaker to Admin and Non admin¶

# get set of speakers
everyone = lambda speaker: True
admins = lambda speaker: speaker.meta["is-admin"]
nonadmins = lambda speaker: not speaker.meta["is-admin"]
split = ["is-admin"]

# compute coordination scores from each admin to everyone
print("Admins, ranked by how much they coordinate to others:")

speaker_to_admin = coord.summarize(wiki_full, everyone, admins, focus="targets")

dicti_counts_admin, dicti_counts_nonadmin = {}, {}

for spkr, score in sorted(speaker_to_admin.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
    dicti_counts_admin[spkr.id] = round(score, 5)

speaker_to_nonadmin = coord.summarize(wiki_full,everyone, nonadmins, focus="targets")

for spkr, score in sorted(speaker_to_nonadmin.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
    dicti_counts_nonadmin [spkr.id] = round(score, 5)

Admins, ranked by how much they coordinate to others:

TOP 10 Speaker who coordinate with admins¶

pd.DataFrame(data = dicti_counts_admin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)

TOP 10 Speaker who coordinate with non- admins¶

pd.DataFrame(data = dicti_counts_nonadmin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)

Find Coordination between Admin and Non admin to Speakers¶

admin_to_spkr = coord.summarize(wiki_full, admins, everyone)

dicti_counts_admin, dicti_counts_nonadmin = {}, {}

for spkr, score in sorted(admin_to_spkr.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
    dicti_counts_admin[spkr.id] = round(score, 5)

nonadmin_to_spkr  = coord.summarize(wiki_full, nonadmins, everyone)

for spkr, score in sorted(nonadmin_to_spkr.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
    dicti_counts_nonadmin[spkr.id] = round(score, 5)

pd.DataFrame(data = dicti_counts_admin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)

Speaker to non-admin coordination¶

pd.DataFrame(data = dicti_counts_nonadmin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)

Basice aggregated Summary statistics¶

coord.summarize(wiki_full, everyone, admins, focus="targets", summary_report=True)

{'marker_agg1': {'article': 0.015555598585512167,
  'auxverb': 0.009663516499465384,
  'conj': 0.013230937289510317,
  'adverb': 0.017329249355186323,
  'ppron': 0.0029675891397264974,
  'ipron': 0.030634938792294442,
  'preps': 0.010898938538928087,
  'quant': 0.020082725608428467},
 'marker_agg2': {'article': 0.010666995155267435,
  'auxverb': 0.00965974179597438,
  'conj': 0.011246236532095569,
  'adverb': 0.010822727131472845,
  'ppron': 0.0006280728061660206,
  'ipron': 0.02307661110412696,
  'preps': 0.007853887946881395,
  'quant': 0.016956733937946604},
 'marker_agg3': {'article': 0.010666995155267435,
  'auxverb': 0.00965974179597438,
  'conj': 0.011246236532095569,
  'adverb': 0.010822727131472845,
  'ppron': 0.0006280728061660206,
  'ipron': 0.02307661110412696,
  'preps': 0.007853887946881395,
  'quant': 0.016956733937946604},
 'agg1': 0.01504543672613146,
 'agg2': 0.011363875801241392,
 'agg3': 0.008886420812259174,
 'count_agg1': 301,
 'count_agg2': 511,
 'count_agg3': 511}

coord.summarize(wiki_full, everyone, nonadmins, focus="targets", summary_report=True)

{'marker_agg1': {'article': 0.008340013138658358,
  'auxverb': 0.007401284196896749,
  'conj': 0.010106910427519265,
  'adverb': 0.007511210484479474,
  'ppron': 0.007713000391147967,
  'ipron': 0.018540313759033426,
  'preps': 0.00677588710100073,
  'quant': 0.027115418874108555},
 'marker_agg2': {'auxverb': 0.008130217413767777,
  'adverb': 0.005881239202070832,
  'ppron': 0.0068253670153056575,
  'ipron': 0.013442437503031372,
  'preps': 0.0018353311750622472,
  'quant': 0.01996582337592379,
  'conj': 0.013765255982772225,
  'article': 0.006747629121977491},
 'marker_agg3': {'auxverb': 0.008130217413767777,
  'adverb': 0.005881239202070832,
  'ppron': 0.0068253670153056575,
  'ipron': 0.013442437503031372,
  'preps': 0.0018353311750622472,
  'quant': 0.01996582337592379,
  'conj': 0.013765255982772225,
  'article': 0.006747629121977491},
 'agg1': 0.011688004796605563,
 'agg2': 0.009574162598738967,
 'agg3': 0.007599102233410238,
 'count_agg1': 647,
 'count_agg2': 1297,
 'count_agg3': 1297}

References¶

[1] Brownlee, J. (2017, September 29). How to Encode Text Data for Machine Learning with scikit-learn [Web log post]. Retrieved April 02, 2022, from https://machinelearningmastery.com/prepare-text-data-machine-learning-scikit-learn/

[2] Chang, J. P., Chiam, C., Fu, L., Wang, A. Z., Zhang, J., & Danescu-Niculescu-Mizil, C. (2020). ConvoKit: A Toolkit for the Analysis of Conversations. doi:10.48550/ARXIV.2005.04246

[3] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953

[4] Danescu-Niculescu-Mizil, C., Lee, L., Pang, B., & Kleinberg, J. (2012). Echoes of power. Proceedings of the 21st International Conference on World Wide Web - WWW '12. https://doi.org/10.1145/2187836.2187931

[5] Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013). A computational approach to politeness with application to social factors. The Association for Computer Linguistics, 250-259. https://doi.org/10.48550/arXiv.1306.6078

[6] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York, NY: R. Springer Publishing Company, Incorporated.

[7] Jurafsky, D., & Martin, J. H. (2022). Speech and Language Processing (3rd ed.) [3rd edition draft]. Retrieved March 25, 2022, from https://web.stanford.edu/~jurafsky/slp3/

[8] https://github.com/CornellNLP/Cornell-Conversational-Analysis-Toolkit/blob/master/examples/vectors/bag-of-words-demo.ipynb

[9] https://github.com/CornellNLP/Cornell-Conversational-Analysis-Toolkit/blob/master/examples/coordination/examples.ipynb

[10] https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/

	timestamp	text	speaker	reply_to	conversation_id	meta.Normalized Score	meta.Binary	meta.Annotations	meta.parsed	vectors
id
629705	NOT_RECORDED	Where did you learn English? How come you're t...	user	None	629705	-1.120049	-1	{'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'Where', 'tag': 'W...	[]
244336	NOT_RECORDED	Thanks very much for your edit to the <url> ar...	user	None	244336	1.313955	1	{'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '...	[{'rt': 0, 'toks': [{'tok': 'Thanks', 'tag': '...	[]
627353	NOT_RECORDED	Sir i think u hav many friends on wiki who can...	user	None	627353	-0.247941	0	{'A233ONYNWKDIYF': 17, 'A2UFD1I8ZO1V4G': 17, '...	[{'rt': 2, 'toks': [{'tok': 'Sir', 'tag': 'NNP...	[]
448565	NOT_RECORDED	I can't find it. Maybe I didn't manage to gue...	user	None	448565	0.058298	0	{'A233ONYNWKDIYF': 17, 'A1TLLJDX8H4JP1': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',...	[]
625810	NOT_RECORDED	I can't spend too much time, and I'm no specia...	user	None	625810	0.346093	0	{'A21753FQKCM5DQ': 17, 'AYG3MF094634L': 14, 'A...	[{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',...	[]

	timestamp	text	speaker	reply_to	conversation_id	meta.Normalized Score	meta.Binary	meta.Annotations	meta.parsed	vectors
id
629705	NOT_RECORDED	Where did you learn English? How come you're t...	user	None	629705	-1.120049	-1	{'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'Where', 'tag': 'W...	[]
244336	NOT_RECORDED	Thanks very much for your edit to the <url> ar...	user	None	244336	1.313955	1	{'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '...	[{'rt': 0, 'toks': [{'tok': 'Thanks', 'tag': '...	[]
627353	NOT_RECORDED	Sir i think u hav many friends on wiki who can...	user	None	627353	-0.247941	0	{'A233ONYNWKDIYF': 17, 'A2UFD1I8ZO1V4G': 17, '...	[{'rt': 2, 'toks': [{'tok': 'Sir', 'tag': 'NNP...	[]
448565	NOT_RECORDED	I can't find it. Maybe I didn't manage to gue...	user	None	448565	0.058298	0	{'A233ONYNWKDIYF': 17, 'A1TLLJDX8H4JP1': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',...	[]
625810	NOT_RECORDED	I can't spend too much time, and I'm no specia...	user	None	625810	0.346093	0	{'A21753FQKCM5DQ': 17, 'AYG3MF094634L': 14, 'A...	[{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',...	[]

	timestamp	text	speaker	reply_to	conversation_id	meta.Normalized Score	meta.Binary	meta.Annotations	meta.parsed	meta.politeness_strategies	meta.politeness_markers	vectors
id
629705	NOT_RECORDED	Where did you learn English? How come you're t...	user	None	629705	-1.120049	-1	{'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]
244336	NOT_RECORDED	Thanks very much for your edit to the <url> ar...	user	None	244336	1.313955	1	{'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '...	[{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]
627353	NOT_RECORDED	Sir i think u hav many friends on wiki who can...	user	None	627353	-0.247941	0	{'A233ONYNWKDIYF': 17, 'A2UFD1I8ZO1V4G': 17, '...	[{'rt': 2, 'toks': [{'tok': 'sir', 'tag': 'NNP...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]
448565	NOT_RECORDED	I can't find it. Maybe I didn't manage to gue...	user	None	448565	0.058298	0	{'A233ONYNWKDIYF': 17, 'A1TLLJDX8H4JP1': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]
625810	NOT_RECORDED	I can't spend too much time, and I'm no specia...	user	None	625810	0.346093	0	{'A21753FQKCM5DQ': 17, 'AYG3MF094634L': 14, 'A...	[{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]

	timestamp	text	speaker	reply_to	conversation_id	meta.Normalized Score	meta.Binary	meta.Annotations	meta.parsed	meta.politeness_strategies	meta.politeness_markers	meta.prediction	meta.pred_score	vectors
id
629705	NOT_RECORDED	Where did you learn English? How come you're t...	user	None	629705	-1.120049	-1	{'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A...	[{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	0	0.153666	[]
244336	NOT_RECORDED	Thanks very much for your edit to the <url> ar...	user	None	244336	1.313955	1	{'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '...	[{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	1	0.989169	[]
214411	NOT_RECORDED	\|style="vertical-align: middle; padding: 3px;"...	user	None	214411	0.619247	1	{'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A...	[{'rt': 20, 'toks': [{'tok': '\|style="vertical...	{'feature_politeness_==Please==': 1, 'feature_...	{'politeness_markers_==Please==': [[('please',...	1	0.870336	[]

	prediction	pred_score
id
622173	0	0.032947
357406	0	0.042074
452664	0	0.050474
626728	0	0.054090
621114	0	0.064721
...	...	...
628941	1	0.994737
213945	1	0.996444
464868	1	0.996929
179380	1	0.998558
358525	1	0.998575

	Averages
Please	0.038594
Please_start	0.006662
HASHEDGE	0.497128
Indirect_(btw)	0.004824
Hedges	0.188146
Factuality	0.057202
Deference	0.013784
Gratitude	0.066391
Apologizing	0.019986
1st_person_pl.	0.102458
1st_person	0.607627
1st_person_start	0.291293
2nd_person	1.022743
2nd_person_start	0.053067
Indirect_(greeting)	0.084999
Direct_question	0.217551
Direct_start	0.092120
HASPOSITIVE	0.653113
HASNEGATIVE	0.461980
SUBJUNCTIVE	0.096715
INDICATIVE	0.065932

	coef
feat_name
feature_politeness_==Gratitude==	1.178122
feature_politeness_==Indirect_(greeting)==	0.638553
feature_politeness_==SUBJUNCTIVE==	0.541239
feature_politeness_==Indirect_(btw)==	0.467033
feature_politeness_==1st_person_start==	0.414873
feature_politeness_==Apologizing==	0.362853
feature_politeness_==HASPOSITIVE==	0.347574
feature_politeness_==Deference==	0.309130
feature_politeness_==INDICATIVE==	0.235271
feature_politeness_==1st_person==	0.209016
feature_politeness_==Please==	0.186717
feature_politeness_==HASHEDGE==	0.126751
feature_politeness_==Hedges==	0.125405
feature_politeness_==1st_person_pl.==	0.107767
feature_politeness_==2nd_person==	0.106503
feature_politeness_==Please_start==	-0.026375
feature_politeness_==2nd_person_start==	-0.123303
feature_politeness_==Direct_start==	-0.275117
feature_politeness_==Direct_question==	-0.279500
feature_politeness_==Factuality==	-0.297774
feature_politeness_==HASNEGATIVE==	-0.419083

	feature_politeness_==1st_person==	feature_politeness_==1st_person_start==	feature_politeness_==2nd_person==	...	feature_politeness_==HASHEDGE==	feature_politeness_==HASNEGATIVE==	feature_politeness_==HASPOSITIVE==	feature_politeness_==Hedges==	feature_politeness_==Please==	feature_politeness_==SUBJUNCTIVE==
620707	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	0.0	0.0
75904	1.0	1.0	1.0	...	0.0	0.0	0.0	0.0	1.0	1.0
619003	0.0	0.0	1.0	...	0.0	1.0	0.0	0.0	0.0	0.0

	Classifier Names	Accuracy Score	Precision Score
0	Logistic Regression	0.764006	0.782908
1	Naive Bayes	0.745648	0.740062
2	Support Vector Machine (Kernel= (RBF))	0.759873	0.791797
3	Support Vector Machine (Kernel = Polynomial)	0.756205	0.807946
4	K-Nearest Neighbour	0.697422	0.728912
5	Decision Tree	0.714873	0.743608
6	MLP(Multi-Layer Preceptron(Relu))	0.735999	0.751663
7	MLP (Multi-Layer Preceptron(Hyberbolic Tangent))	0.725436	0.735790
8	Perceptron	0.708900	0.669490

	timestamp	text	speaker	reply_to	conversation_id	meta.Normalized Score	meta.Binary	meta.Annotations	meta.parsed	meta.politeness_strategies	meta.politeness_markers	vectors
id
0	NOT_RECORDED	Can you explain more in detail, what should I ...	user	None	0	0.217326	0	{'A33SMNMTMIOJ6T': 12, 'A2OXXHGAM7B0Y': 16, 'A...	[{'rt': 2, 'toks': [{'tok': 'Can', 'tag': 'MD'...	NaN	NaN	[]
1	NOT_RECORDED	Will expressions always be unambiguously paren...	user	None	1	0.063302	0	{'A1UIH2IMG9DV95': 13, 'A23FB7HE970AZJ': 13, '...	[{'rt': 13, 'toks': [{'tok': 'Will', 'tag': 'M...	NaN	NaN	[]
2	NOT_RECORDED	how are you resolving function pointers? I am ...	user	None	2	0.128902	0	{'A1BS64O3JY0YJ4': 13, 'A2AE4MZVUX9JPX': 15, '...	[{'rt': 3, 'toks': [{'tok': 'how', 'tag': 'WRB...	NaN	NaN	[]
3	NOT_RECORDED	What is the definition of `buffer`? Is it a lo...	user	None	3	0.240188	0	{'A3VJDU2VRMN05L': 13, 'A1ZHP80O13CEUI': 13, '...	[{'rt': 1, 'toks': [{'tok': 'What', 'tag': 'WP...	NaN	NaN	[]
4	NOT_RECORDED	Is `A` a global variable? What is x?	user	None	4	0.508284	1	{'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '...	[{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[bow]
...	...	...	...	...	...	...	...	...	...	...	...	...
6598	NOT_RECORDED	hello. I do not know if others, but to me your...	user	None	6598	0.088219	0	{'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 13, '...	[{'rt': 0, 'toks': [{'tok': 'hello', 'tag': 'U...	NaN	NaN	[]
6599	NOT_RECORDED	That sounds amazing!! Can I have the link? :D	user	None	6599	0.449894	1	{'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 17, '...	[{'rt': 1, 'toks': [{'tok': 'that', 'tag': 'DT...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[bow]
6600	NOT_RECORDED	Is it easy to use the small pump? Have you not...	user	None	6600	-0.268236	0	{'A233ONYNWKDIYF': 13, 'A3S5L3I8O3Q2G': 13, 'A...	[{'rt': 0, 'toks': [{'tok': 'Is', 'tag': 'VBZ'...	NaN	NaN	[]
6601	NOT_RECORDED	Cool idea. I'm curious, though, how can one dr...	user	None	6601	0.526613	1	{'A1E0EK09CA5OIO': 19, 'A233ONYNWKDIYF': 13, '...	[{'rt': 1, 'toks': [{'tok': 'cool', 'tag': 'UH...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[bow]
6602	NOT_RECORDED	I've never even questioned this before, but no...	user	None	6602	-0.159096	0	{'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 9, 'A...	[{'rt': 4, 'toks': [{'tok': 'I', 'tag': 'PRP',...	NaN	NaN	[]

	feature_politeness_==1st_person==	feature_politeness_==1st_person_pl.==	feature_politeness_==1st_person_start==	feature_politeness_==2nd_person==	feature_politeness_==2nd_person_start==	feature_politeness_==Apologizing==	feature_politeness_==Deference==	feature_politeness_==Direct_question==	feature_politeness_==Direct_start==	feature_politeness_==Factuality==	...	feature_politeness_==HASNEGATIVE==	feature_politeness_==HASPOSITIVE==	feature_politeness_==Hedges==	feature_politeness_==INDICATIVE==	feature_politeness_==Indirect_(btw)==	feature_politeness_==Indirect_(greeting)==	feature_politeness_==Please==	feature_politeness_==Please_start==	feature_politeness_==SUBJUNCTIVE==	meta.Binary
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1
5	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0
6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0
9	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1
11	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6595	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1
6596	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1
6597	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1
6599	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1
6601	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1

	...	your	meta.Binary
4	...	0	1
5	...	1	-1
6	...	0	-1
9	...	1	1
11	...	0	1

	!	!vote	!voter	"	"#redirect"	"'''bong'''warrior"?	"''<url>''	"''images	"(this	"...definitions...".	...	zhongda	zim	zionist	zooms	zuu	\|	\|2	\|30em	\|style="vertical-align:	\|}
629705	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
244336	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
214411	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.821102	0.0	0.0	0.821102	0.0
177439	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
341534	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
60798	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
156734	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
147665	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
234095	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0
563032	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.000000	0.0

	Cross Validation Mean score
Standard Logistic Regression	0.747017
Mutinomial Naive Bayes	0.702489
Decision Tree	0.708896
KNeighbors Classifiers	0.511028
SVC RBF	0.654288
SVC Poly	0.500461
MLP (relu)	0.742422
MLP(tanh)	0.732797
Perceptron	0.722659

	prediction	pred_score
id
620707	True	1.0
62454	True	1.0
153143	True	1.0
201933	True	1.0
486441	True	1.0

	...	your	meta.Binary
4	...	0	1
5	...	1	-1
6	...	0	-1
9	...	1	1
11	...	0	1

	!	!!	!=	"	""	""this	"$thing->edit	"(pseudo)	"*i	"+1"	...	μf,	—	“blog”?	“good”	“ie6	“k-cnf”	“section”	“synchronize	ℽℼℾℿ	か
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
9	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
11	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	Cross Validation Mean score
Logisitc Regression	0.594489
Naive Bayes	0.606306
Support Vector Machine (Kernel= (RBF))	0.570564
Support Vector Machine (Kernel = Polynomial)	0.553598
K-Nearest Neighbour	0.502103
Decision Tree	0.503640
MLP(Multi-Layer Preceptron(Relu))	0.558448
MLP (Multi-Layer Preceptron(Hyberbolic Tangent))	0.588748
Perceptron	0.586320

	!	!!	!=	"	""	""this	"$thing->edit	"(pseudo)	"*i	"+1"	...	μf,	—	“blog”?	“good”	“ie6	“k-cnf”	“section”	“synchronize	ℽℼℾℿ	か
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
9	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
11	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	timestamp	text	speaker	reply_to	conversation_id	meta.is-admin	meta.parsed	meta.politeness_strategies	meta.politeness_markers	vectors
id
524288	1.189190940E09	You should look at all of the point on the tem...	Frightner	None	524288	False	[{'rt': 20, 'toks': [{'tok': 'you', 'tag': 'PR...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]
524289	1.189204860E09	Yes I agree. The law permits usage of document...	Revizionist	None	524288	False	[{'rt': 2, 'toks': [{'tok': 'yes', 'tag': 'UH'...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]
1	1.310744280E09	Yes, that's good. Revathy's page looked very r...	Johannes003	None	1	False	[{'rt': 3, 'toks': [{'tok': 'yes', 'tag': 'UH'...	{'feature_politeness_==Please==': 0, 'feature_...	{'politeness_markers_==Please==': [], 'politen...	[]

	vectors	meta.is-admin	meta.edit-count	meta.coord
id
Frightner	[]	False	1322	NaN
Revizionist	[]	False	1716	NaN
Johannes003	[]	False	8283	NaN
Michael-Billa	[]	False	396	NaN
AnonEMouse	[]	True	13150	{'Kwork': {'article': 0.0, 'auxverb': 0.0, 'co...
...	...	...	...	...
{unknown-1133}	[]	False	unknown	NaN
Kelly Martin	[]	True	17726	NaN
Cretanpride	[]	False	192	NaN
CaveatLector	[]	False	2196	NaN
Huntster	[]	True	27608	NaN

	feature_politeness_==Please==	feature_politeness_==Please_start==	feature_politeness_==HASHEDGE==	feature_politeness_==Indirect_(btw)==	feature_politeness_==Hedges==	feature_politeness_==Factuality==	feature_politeness_==Deference==	feature_politeness_==Gratitude==	feature_politeness_==Apologizing==	feature_politeness_==1st_person_pl.==	...	feature_politeness_==1st_person_start==	feature_politeness_==2nd_person==	feature_politeness_==2nd_person_start==	feature_politeness_==Indirect_(greeting)==	feature_politeness_==Direct_question==	feature_politeness_==Direct_start==	feature_politeness_==HASPOSITIVE==	feature_politeness_==HASNEGATIVE==	feature_politeness_==SUBJUNCTIVE==	feature_politeness_==INDICATIVE==
524288	0	0	1	0	0	1	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
524289	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	1	0	0	0
2	0	0	1	0	1	0	0	0	0	0	...	1	0	0	0	0	0	1	0	0	0
3	0	0	1	0	1	1	0	0	0	1	...	1	1	0	0	0	0	1	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
266783	0	0	1	0	1	0	0	0	0	0	...	1	1	0	0	1	0	1	0	0	0
552089	0	0	1	0	1	0	0	0	0	0	...	0	0	0	0	0	0	1	1	0	0
552090	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
289946	0	0	1	0	1	0	0	0	0	0	...	0	1	0	0	0	0	0	1	0	0
289947	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	Speaker name	Average Pair Wise Score
0	Leyo	0.27500
1	Dlohcierekim	0.25000
2	Raul654	0.21000
3	Stevenfruitsmaak	0.20833
4	Tznkai	0.18571
5	Magioladitis	0.17202
6	Casliber	0.15774
7	Hu12	0.15625
8	Bhadani	0.15238
9	Guettarda	0.15000

	Speaker name	Average Pair Wise Score
0	Ocaasi	0.33333
1	Ekki01	0.27083
2	PANONIAN	0.26667
3	Leaky_caldron	0.25333
4	Penyulap	0.25000
5	KrebMarkt	0.25000
6	VKokielov	0.25000
7	Mjb	0.25000
8	Armbrust	0.23810
9	Mdcollins1984	0.20089

	Speaker name	Average Pair Wise Score
0	Gatoclass	0.20278
1	DarkFalls	0.16667
2	DO11.10	0.15476
3	AnemoneProjectors	0.15238
4	Amalas	0.14796
5	Rogerd	0.11944
6	Josiah Rowe	0.11490
7	KillerChihuahua	0.11151
8	Guettarda	0.11042
9	Black Falcon	0.10417

	...	your	meta.Binary
4	...	0	1
5	...	1	-1
6	...	0	-1
9	...	1	1
11	...	0	1

	!	!!	!=	"	""	""this	"$thing->edit	"(pseudo)	"*i	"+1"	...	μf,	—	“blog”?	“good”	“ie6	“k-cnf”	“section”	“synchronize	ℽℼℾℿ	か
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
9	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
11	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	!	!!	!=	"	""	""this	"$thing->edit	"(pseudo)	"*i	"+1"	...	μf,	—	“blog”?	“good”	“ie6	“k-cnf”	“section”	“synchronize	ℽℼℾℿ	か
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
9	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
11	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	Speaker name	Average Pair Wise Score
0	Howard the Duck	0.19792
1	Lugnuts	0.17708
2	WLU	0.17542
3	Wikidudeman	0.16667
4	Tagishsimon	0.16667
5	Wwheaton	0.16667
6	JJ Harrison	0.16429
7	PANONIAN	0.15833
8	Fowler&fowler	0.15714
9	Ed	0.15000